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fix network grey pies (malariagen#298)
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@alimanfoo
alimanfoo authored Nov 10, 2022
1 parent cb43f31 commit 986e7e2
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Showing 2 changed files with 96 additions and 30 deletions.
43 changes: 28 additions & 15 deletions malariagen_data/ag3.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -6346,23 +6346,32 @@ def plot_haplotype_network(

debug("setup colors")
color_values = None
color_values_display = None
color_discrete_map = None
color_discrete_map_display = None
ht_color_counts = None
color_params = None
if color is not None:

# sanitise color column - necessary to avoid grey pie chart segments
df_haps["partition"] = df_haps[color].str.replace(r"\W", "", regex=True)

# extract all unique values of the color column
color_values = df_haps[color].unique()
color_values = df_haps["partition"].unique()
color_values_mapping = dict(zip(df_haps["partition"], df_haps[color]))
color_values_display = [color_values_mapping[c] for c in color_values]

# count color values for each distinct haplotype
ht_color_counts = [
df_haps.iloc[list(s)][color].value_counts().to_dict()
df_haps.iloc[list(s)]["partition"].value_counts().to_dict()
for s in ht_distinct_sets
]

if color == "taxon":
# special case, standardise taxon colors
# special case, standardise taxon colors and order
color_params = self._setup_taxon_colors()
color_discrete_map = color_params["color_discrete_map"]
color_discrete_map_display = color_discrete_map
category_orders = color_params["category_orders"]

elif color_discrete_map is None:

Expand All @@ -6377,10 +6386,11 @@ def plot_haplotype_network(
color_discrete_map = {
v: c for v, c in zip(color_values, cycle(color_discrete_sequence))
}

color_params = {
"color_discrete_map": color_discrete_map,
"category_orders": category_orders,
color_discrete_map_display = {
v: c
for v, c in zip(
color_values_display, cycle(color_discrete_sequence)
)
}

debug("construct graph")
Expand Down Expand Up @@ -6420,6 +6430,7 @@ def plot_haplotype_network(
node_stylesheet["style"][
f"pie-{i + 1}-background-size"
] = f"mapData({v}, 0, 100, 0, 100)"
debug(node_stylesheet)

debug("define edge style")
edge_stylesheet = {
Expand All @@ -6441,8 +6452,9 @@ def plot_haplotype_network(
if color is not None:
legend_fig = plotly_discrete_legend(
color=color,
color_values=color_values,
**color_params,
color_values=color_values_display,
color_discrete_map=color_discrete_map_display,
category_orders=category_orders,
)
legend_component = dcc.Graph(
id="legend",
Expand Down Expand Up @@ -6488,7 +6500,7 @@ def plot_haplotype_network(

debug("create dash app")
app = JupyterDash(
"dash cytoscape network",
"dash-cytoscape-network",
# this stylesheet is used to provide support for a rows and columns
# layout of the components
external_stylesheets=["https://codepen.io/chriddyp/pen/bWLwgP.css"],
Expand Down Expand Up @@ -6522,17 +6534,18 @@ def plot_haplotype_network(
"define a callback function to display information about the selected node"
)

# FIXME: function name should be lowercase
@app.callback(Output("output", "children"), Input("cytoscape", "tapNodeData"))
def displayTapNodeData(data):
def display_tap_node_data(data):
if data is None:
return "Click or tap a node for more information."
else:
n = data["count"]
text = f"No. haplotypes: {n}"
selected_color_data = {
color_v: int(data.get(color_v, 0) * n / 100)
for color_v in color_values
color_v_display: int(data.get(color_v, 0) * n / 100)
for color_v, color_v_display in zip(
color_values, color_values_display
)
}
selected_color_data = sorted(
selected_color_data.items(), key=lambda item: item[1], reverse=True
Expand Down
83 changes: 68 additions & 15 deletions notebooks/plot_haplotype_networks.ipynb
View file
Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 1,
"id": "9de62268",
"metadata": {},
"outputs": [],
Expand All @@ -12,7 +12,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 2,
"id": "0ebfe96b",
"metadata": {},
"outputs": [
Expand Down Expand Up @@ -387,7 +387,7 @@
"https://malariagen.github.io/vector-data/ag3/api.html"
]
},
"execution_count": 3,
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -411,14 +411,14 @@
},
{
"cell_type": "code",
"execution_count": 5,
"id": "97bcbded",
"execution_count": 3,
"id": "1f6bb7f1",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cfc28e2f7373485587583b33d24a5de2",
"model_id": "41fc6aa49e3542c5bd89bfaa04ae4f10",
"version_major": 2,
"version_minor": 0
},
Expand All @@ -432,7 +432,60 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5bb7dea746b7406381a62595e8980937",
"model_id": "164e6ca8218549668f5edb6467b365e9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Load haplotypes: 0%| | 0/50 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
" <iframe\n",
" width=\"100%\"\n",
" height=\"650\"\n",
" src=\"http://127.0.0.1:8049/\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" \n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x7febcebb3250>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ag3.plot_haplotype_network(\n",
" region=\"2L:2,358,158-2,431,617\",\n",
" analysis=\"gamb_colu\",\n",
" sample_query=\"taxon == 'coluzzii'\",\n",
" color=\"country\",\n",
" max_dist=2,\n",
" server_port=8049,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "97bcbded",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "347be210f1c044a490398b50079885fd",
"version_major": 2,
"version_minor": 0
},
Expand All @@ -458,7 +511,7 @@
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x7f7b2bf78910>"
"<IPython.lib.display.IFrame at 0x7febcd279650>"
]
},
"metadata": {},
Expand All @@ -479,14 +532,14 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 5,
"id": "8200a1cb",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "897caefe06ea4e65b949a3268783d265",
"model_id": "7f4d5054344d4341bef567fb50fff4c6",
"version_major": 2,
"version_minor": 0
},
Expand All @@ -512,7 +565,7 @@
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x7f7bb4101f50>"
"<IPython.lib.display.IFrame at 0x7febcd2510d0>"
]
},
"metadata": {},
Expand All @@ -533,14 +586,14 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 6,
"id": "b525b324",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "98727464fa47452387c8cba516223dad",
"model_id": "0d7f7f3c5c2e4ca2bb45993d5f07604d",
"version_major": 2,
"version_minor": 0
},
Expand All @@ -566,7 +619,7 @@
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x7f7bb5d13390>"
"<IPython.lib.display.IFrame at 0x7febcd24dfd0>"
]
},
"metadata": {},
Expand Down Expand Up @@ -610,7 +663,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.14"
"version": "3.7.15"
}
},
"nbformat": 4,
Expand Down

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