def color_consensuses_table_cells(jsonified_partial_consensustable, jsonified_consensus_tree):
if not jsonified_partial_consensustable or not jsonified_consensus_tree:
return []
partial_consensustable_data = tools.unjsonify_df(jsonified_partial_consensustable)
consensustree_data = tools.unjsonify_builtin_types(jsonified_consensus_tree)
tree = consensustree.dict_to_tree(consensustree_data)
return consensustable.get_cells_styling(tree, partial_consensustable_data)
def update_current_tree_state(jsonified_full_consensustree):
if not jsonified_full_consensustree:
return []
full_consensustree_data = json.loads(jsonified_full_consensustree)
full_consensus_tree = consensustree.dict_to_tree(full_consensustree_data)
current_consensustree_data = consensustree.tree_to_dict(
full_consensus_tree)
return json.dumps(current_consensustree_data)
def update_current_tree_state(jsonified_full_consensustree):
if not jsonified_full_consensustree:
return []
full_consensustree_data = tools.unjsonify_builtin_types(
jsonified_full_consensustree)
full_consensus_tree = consensustree.dict_to_tree(full_consensustree_data)
current_consensustree_data = consensustree.tree_to_dict(
full_consensus_tree)
return tools.jsonify_builtin_types(current_consensustree_data)
def update_partial_table_data(jsonified_full_consensustable: str, jsonified_tree: str, slider_value: float):
if not jsonified_full_consensustable or not jsonified_tree:
return []
full_consensustable_data = tools.unjsonify_df(jsonified_full_consensustable)
full_consensustree_data = tools.unjsonify_builtin_types(jsonified_tree)
full_consensustree_tree = consensustree.dict_to_tree(full_consensustree_data)
table_without_consensuses_smaller_than_slider = consensustable.remove_smaller_than_slider(full_consensustable_data,
full_consensustree_tree,
slider_value)
return tools.jsonify_df(table_without_consensuses_smaller_than_slider)
def to_consensustree_graph(jsonified_current_consensustree, leaf_info,
jsonified_full_consensustable):
if not jsonified_current_consensustree or not jsonified_full_consensustable:
return {}
current_consensustree_data = json.loads(jsonified_current_consensustree)
current_consensustree_tree = consensustree.dict_to_tree(
current_consensustree_data)
full_consensustable_data = pd.read_json(jsonified_full_consensustable)
return consensustree.get_consensustree_graph(current_consensustree_tree,
leaf_info,
full_consensustable_data)
def update_partial_table_data(jsonified_full_consensustable: str, jsonified_tree: str,
slider_value: float):
if not jsonified_full_consensustable or not jsonified_tree:
return []
full_consensustable_data = pd.read_json(jsonified_full_consensustable)
full_consensustree_data = json.loads(jsonified_tree)
full_consensustree_tree = consensustree.dict_to_tree(full_consensustree_data)
table_without_consensuses_smaller_than_slider = consensustable.remove_smaller_than_slider(
full_consensustable_data,
full_consensustree_tree,
slider_value)
return table_without_consensuses_smaller_than_slider.to_json()
def to_consensus_node_details_table(tree_click_data,
jsonified_full_consensustable,
jsonified_consensustree):
if not jsonified_full_consensustable or not tree_click_data:
return []
clicked_node = tree_click_data['points'][0]
node_id = clicked_node['pointIndex']
full_consensustable = tools.unjsonify_df(jsonified_full_consensustable)
consensustree_data = tools.unjsonify_builtin_types(jsonified_consensustree)
tree = consensustree.dict_to_tree(consensustree_data)
node_details_df = consensustable.get_consensus_details_df(
node_id, full_consensustable, tree)
return tools.jsonify_df(node_details_df)
def to_consensustree_graph(jsonified_current_consensustree, slider_value,
leaf_info, jsonified_full_consensustable):
if not jsonified_current_consensustree or not jsonified_full_consensustable:
return {}
current_consensustree_data = tools.unjsonify_builtin_types(
jsonified_current_consensustree)
current_consensustree_tree = consensustree.dict_to_tree(
current_consensustree_data)
full_consensustable_data = tools.unjsonify_df(
jsonified_full_consensustable)
return consensustree.get_consensustree_graph(current_consensustree_tree,
slider_value, leaf_info,
full_consensustable_data)
def update_consensus_table(jsonified_partial_consensustable, jsonified_consensus_tree):
if not jsonified_partial_consensustable:
# return [], [{}], []
return [{"ID": 0}, {"ID": 1}], [{"name": i, "id": i} for i in ["ID"]], []
partial_consensustable_data = pd.read_json(jsonified_partial_consensustable)
consensustable_columns = [{"name": i, "id": i} for i in partial_consensustable_data.columns]
consensustable_content = partial_consensustable_data.to_dict("rows")
if not jsonified_consensus_tree:
return consensustable_content, consensustable_columns, []
consensustree_data = json.loads(jsonified_consensus_tree)
tree = consensustree.dict_to_tree(consensustree_data)
color_cells = consensustable.get_cells_styling(tree, partial_consensustable_data)
return consensustable_content, consensustable_columns, color_cells
def get_sankey_diagram(self, hidde, zoom_out, slider_values, highlight_seq,
click_data, checklist, threshold,
consensustable_data, consensustree_data):
if not self.sequences:
raise PreventUpdate()
# RANGE START / END
range_start = min(self.column_dict[slider_values[0]])
range_end = max(self.column_dict[slider_values[1]]
) if slider_values[1] in self.column_dict else len(
self.diagram)
label = []
source = []
target = []
value = []
link_color = []
colors = dict(A="#FF9AA2", C="#B5EAD7", G="#C7CEEA", T="#FFDAC1")
# FILTER SEQUENCES (AFFINITY TREE)
if click_data:
tree_node_id = click_data['points'][0]['pointIndex']
full_consensustable = pd.read_json(consensustable_data)
consensustree_data = json.loads(consensustree_data)
tree = consensustree.dict_to_tree(consensustree_data)
node_details_df = consensustable.get_consensus_details_df(
tree_node_id, full_consensustable, tree)
filtered_sequences = node_details_df["SEQID"].tolist()
diagram_filtered = self.construct_diagram(sequences_values=[
self.sequences[seq] for seq in filtered_sequences
])
for i in range(len(self.column_dict)):
if i not in diagram_filtered:
diagram_filtered[i] = dict(
base="",
sources={},
targets={},
)
else:
tree_node_id = None
filtered_sequences = list(self.sequences.keys())
diagram_filtered = copy.deepcopy(self.diagram)
if zoom_out: # EXTREAME ZOOM-OUT
checklist = [1, 2]
threshold = len(self.sequences) * 0.2
range_start = 0
range_end = len(self.column_dict) - 1
if 3 in checklist:
sequences_values = [
self.sequences[seq] for seq in filtered_sequences
]
new_sequences_values = [
self._remove_snp(self.consensus_sequence, sequence)
for sequence in sequences_values
]
diagram_filtered = self.construct_diagram(
sequences_values=new_sequences_values)
for i in range(len(self.diagram)):
if i not in diagram_filtered:
diagram_filtered[i] = dict(
base="",
sources={},
targets={},
)
if 2 in checklist and threshold > 0: # WEAK CONNECTIONS
weak_nodes = list()
for node_id in sorted(
diagram_filtered.keys())[range_start:range_end + 1]:
node = diagram_filtered[node_id]
if node["sources"] and (all([
x in weak_nodes for x in node["sources"].keys()
]) or all([x <= threshold for x in node["sources"].values()])):
weak_nodes.append(node_id)
diagram_filtered[node_id]["sources"] = {}
diagram_filtered[node_id]["targets"] = {}
else:
diagram_filtered[node_id]["sources"] = {
key: value
for key, value in node["sources"].items()
if value > threshold
}
diagram_filtered[node_id]["targets"] = {
key: value
for key, value in node["targets"].items()
if value > threshold
}
for node_id in sorted(diagram_filtered.keys())[range_end -
1:range_start:-1]:
node = diagram_filtered[node_id]
if node["targets"] and all(
[x in weak_nodes for x in node["targets"].keys()]):
weak_nodes.append(node_id)
diagram_filtered[node_id]["sources"] = {}
diagram_filtered[node_id]["targets"] = {}
else:
diagram_filtered[node_id]["sources"] = {
key: value
for key, value in node["sources"].items()
if key not in weak_nodes
}
diagram_filtered[node_id]["targets"] = {
key: value
for key, value in node["targets"].items()
if key not in weak_nodes
}
if 1 in checklist: # CONCAT VERTICLES
diagram_filtered, diagram_reorganization = self._bound_vertices(
diagram_filtered, range_start, range_end)
else:
diagram_reorganization = dict()
if highlight_seq and highlight_seq in filtered_sequences: # HIGHLIGHT SEQUENCE
highlight_seq_nodes = [
node_id for node_id in self.sequences[highlight_seq]
if node_id not in diagram_reorganization.keys()
]
else:
highlight_seq_nodes = []
for node_id in sorted(diagram_filtered.keys())[range_start:range_end +
1]:
label.append(diagram_filtered[node_id]["base"])
for t in diagram_filtered[node_id]["targets"]:
if t <= range_end:
source.append(node_id - range_start)
target.append(t - range_start)
value.append(diagram_filtered[node_id]["targets"][t])
link_color.append("#D3D3D3")
# HIGHLIGHT SEQUENCE
if highlight_seq_nodes and node_id in highlight_seq_nodes:
s_id = highlight_seq_nodes.index(node_id)
if highlight_seq_nodes[s_id + 1] == t:
value[-1] -= 1
source.append(node_id - range_start)
target.append(t - range_start)
value.append(1)
link_color.append("#342424")
colors = dict(A="#FF9AA2", C="#B5EAD7", G="#C7CEEA", T="#FFDAC1")
fig = go.Figure(
data=go.Sankey(
arrangement="snap",
node=dict(label=[
l if len(l) < 5 else f"{l[0]}...{l[-1]}({len(l)})"
for l in label
],
pad=10,
color=[
colors[l] if l in colors else "gray"
for l in label
]),
link=dict(source=source,
target=target,
value=value,
color=link_color)),
layout=dict(
# height=300,
# width=1600
))
return fig, str(tree_node_id)