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31d5c57 a233044 3bc93b2 31d5c57 a233044 3bc93b2 31d5c57 a233044 31d5c57 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 | import streamlit as st
import scanpy as sc
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import logging
import textwrap
from .utils import display_plot_with_download, display_interactive_spatial_plot, display_plotly_with_download
logger = logging.getLogger(__name__)
def init_plot_state():
"""Initialize plot caching state variables."""
if "plot_cache" not in st.session_state:
st.session_state.plot_cache = {}
if "last_viz_params" not in st.session_state:
st.session_state.last_viz_params = {}
def _detect_viz_change_and_clear():
"""Detect if visualization parameters changed and clear matplotlib cache."""
current_params = {
'viz_choice': st.session_state.get('sp_viz_choice', 'Domains'),
'plot_mode': st.session_state.get('sp_mode', 'Static'),
}
last_params = st.session_state.get('sp_last_params', {})
if current_params != last_params:
st.session_state.sp_last_params = current_params
plt.close('all') # Close all matplotlib figures
return True
return False
def render_spatial_flux_map(metabolic_adata):
"""Render spatial flux maps with Red theme."""
init_plot_state()
_detect_viz_change_and_clear()
st.markdown("<h2 style='color: #d32f2f;'><i class='fas fa-map-location-dot'></i> Spatial Metabolic flux</h2>", unsafe_allow_html=True)
viz_choice = st.session_state.get("sp_viz_choice", "Domains")
if viz_choice == "Domains":
c1, c2, c3 = st.columns([1.5, 1.2, 1.3])
else:
c1, c2, c3, c4 = st.columns([1.2, 1.8, 1.0, 1.2])
with c1:
viz_choice = st.selectbox("Analysis Type:", options=["Domains", "Reactions", "Pathways"], key="sp_viz_choice")
with (c3 if viz_choice == "Domains" else c4):
plot_mode = st.radio("Plot Mode:", ["Static", "Interactive"], horizontal=True, key="sp_mode")
with (c2 if viz_choice == "Domains" else c3):
spot_size = st.slider("Spot Size:", 0.5, 5.0, 1.2, 0.5, key="sp_spot_size_static") if plot_mode == "Static" else st.slider("Spot Size:", 1, 20, 6, key="sp_spot_size_interactive")
selected_items = []
if viz_choice != "Domains":
with c2:
if viz_choice == "Reactions":
if 'rxn_full_names' in metabolic_adata.var.columns:
unique_names = {}
for idx, row in metabolic_adata.var.iterrows():
f_name = str(row['rxn_full_names'])
if f_name not in unique_names:
unique_names[f_name] = idx
rx_options = sorted(list(unique_names.keys()))
if plot_mode == "Interactive":
sel_name = st.selectbox("Select Reaction:", options=rx_options, key="sp_rx_single")
selected_items = [unique_names[sel_name]] if sel_name else []
else:
sel_names = st.multiselect("Select Reactions:", options=rx_options, default=rx_options[:1], key="sp_rx_multi")
selected_items = [unique_names[n] for n in sel_names if n in unique_names]
else:
rx_options = metabolic_adata.var_names.tolist()
if plot_mode == "Interactive":
sel = st.selectbox("Select Reaction:", options=rx_options, key="sp_rx_single")
selected_items = [sel] if sel else []
else:
selected_items = st.multiselect("Select Reactions:", options=rx_options, default=rx_options[:1], key="sp_rx_multi")
elif viz_choice == "Pathways":
if 'subsystems' in metabolic_adata.var.columns:
path_options = sorted([p for p in metabolic_adata.var['subsystems'].unique() if pd.notna(p)])
if plot_mode == "Interactive":
sel = st.selectbox("Select Pathway:", options=path_options, key="sp_path_single")
selected_items = [sel] if sel else []
else:
selected_items = st.multiselect("Select Pathways:", options=path_options, default=path_options[:1], key="sp_path_multi")
else:
st.warning("No pathway data.")
try:
library_id = next(iter(metabolic_adata.uns["spatial"]))
img_key = "hires" if "hires" in metabolic_adata.uns["spatial"][library_id]["images"] else "downscaled_fullres"
if viz_choice == "Domains":
if plot_mode == "Interactive":
display_interactive_spatial_plot(
metabolic_adata,
color_key="domain",
spot_size=spot_size,
plot_name="spatial_domain_plotly",
title="Domain Assignment",
help_text="This map highlights the spatial domains assigned byclustering spots with similar metabolic flux patterns. It shows the geographical organization of the tissue's metabolic environment."
)
else:
fig, ax = plt.subplots(figsize=(10, 8))
sc.pl.spatial(metabolic_adata, img_key=img_key, color=['domain'], size=spot_size, show=False, ax=ax)
display_plot_with_download(
fig,
"spatial_domain",
help_text="This map shows the spatial distribution of metabolic domains across the tissue. Each domain represents a cluster of spots with similar metabolic flux profiles."
)
plt.close(fig)
elif viz_choice == "Pathways":
if not selected_items:
st.info("Please select a pathway.")
return
if plot_mode == "Interactive":
target = selected_items[0]
rx_list = metabolic_adata.var[metabolic_adata.var['subsystems'] == target].index.tolist()
X_sub = metabolic_adata[:, rx_list].X
pathway_avg = np.array(X_sub.mean(axis=1)).flatten() if not hasattr(X_sub, "toarray") else np.array(X_sub.toarray().mean(axis=1)).flatten()
metabolic_adata.obs[f'temp_{target}'] = pathway_avg
wrapper = textwrap.TextWrapper(width=40)
display_title = wrapper.fill(text=f"Pathway: {target}")
display_interactive_spatial_plot(
metabolic_adata,
color_key=f'temp_{target}',
spot_size=spot_size,
plot_name=f"spatial_{target}_avg_plotly",
title=display_title,
help_text=f"This interactive map shows the averaged flux distribution for the **{target}** pathway. High intensity regions highlight where this metabolic process is most active within the tissue."
)
del metabolic_adata.obs[f'temp_{target}']
else:
per_page = 4
total = len(selected_items)
pages = (total + per_page - 1) // per_page
if "sp_path_page" not in st.session_state: st.session_state.sp_path_page = 1
if st.session_state.sp_path_page > pages: st.session_state.sp_path_page = 1
curr_items = selected_items[(st.session_state.sp_path_page-1)*per_page : st.session_state.sp_path_page*per_page]
n_cols = 2 if len(curr_items) > 1 else 1
n_rows = (len(curr_items) + n_cols - 1) // n_cols
fig, axes = plt.subplots(n_rows, n_cols, figsize=(8*n_cols, 7*n_rows))
if len(curr_items) == 1: axes = np.array([[axes]])
elif n_rows == 1: axes = axes.reshape(1, -1)
elif n_cols == 1: axes = axes.reshape(-1, 1)
for i, target in enumerate(curr_items):
r, c = i // n_cols, i % n_cols
rx_list = metabolic_adata.var[metabolic_adata.var['subsystems'] == target].index.tolist()
X_sub = metabolic_adata[:, rx_list].X
avg = np.array(X_sub.mean(axis=1)).flatten() if not hasattr(X_sub, "toarray") else np.array(X_sub.toarray().mean(axis=1)).flatten()
metabolic_adata.obs['tmp_avg'] = avg
sc.pl.spatial(metabolic_adata, img_key=img_key, color=['tmp_avg'], size=spot_size, cmap='jet', show=False, ax=axes[r,c])
wrapper = textwrap.TextWrapper(width=40)
axes[r,c].set_title(wrapper.fill(text=str(target)), fontsize=12)
for j in range(len(curr_items), n_rows*n_cols): axes[j//n_cols, j%n_cols].axis('off')
plt.tight_layout()
target_names = ", ".join([str(t) for t in curr_items])
display_plot_with_download(
fig,
f"spatial_pathway_p{st.session_state.sp_path_page}",
help_text=f"This spatial flux map visualizes the spatial distribution of averaged flux for the pathways: **{target_names}**. It helps localize pathway activities within the tissue."
)
plt.close(fig)
if 'tmp_avg' in metabolic_adata.obs: del metabolic_adata.obs['tmp_avg']
if pages > 1:
c_p1, c_p2, c_p3 = st.columns([1,2,1])
if c_p1.button("Prev Pathway", key="pw_prev"): st.session_state.sp_path_page -= 1; st.rerun()
c_p2.markdown(f"<center>Pathway Page {st.session_state.sp_path_page} / {pages}</center>", unsafe_allow_html=True)
if c_p3.button("Next Pathway", key="pw_next"): st.session_state.sp_path_page += 1; st.rerun()
elif selected_items:
if plot_mode == "Interactive":
target = selected_items[0]
display_title = target
if 'rxn_full_names' in metabolic_adata.var.columns and target in metabolic_adata.var_names:
display_title = metabolic_adata.var.loc[target, 'rxn_full_names']
wrapper = textwrap.TextWrapper(width=40)
display_interactive_spatial_plot(
metabolic_adata,
color_key=target,
spot_size=spot_size,
plot_name=f"spatial_{target}_plotly",
title=wrapper.fill(text=f"Reaction: {display_title}"),
help_text=f"This interactive spatial map visualizes the flux distribution for the reaction **{display_title}**. You can explore its metabolic activity across different spatial domains."
)
else:
per_page = 8
total = len(selected_items)
pages = (total + per_page - 1) // per_page
if "spatial_flux_page" not in st.session_state: st.session_state.spatial_flux_page = 1
if st.session_state.spatial_flux_page > pages: st.session_state.spatial_flux_page = 1
curr_rx = selected_items[(st.session_state.spatial_flux_page-1)*per_page : st.session_state.spatial_flux_page*per_page]
n_cols = min(2, len(curr_rx))
n_rows = (len(curr_rx) + n_cols - 1) // n_cols
fig, axes = plt.subplots(n_rows, n_cols, figsize=(8*n_cols, 7*n_rows))
if len(curr_rx) == 1: axes = np.array([[axes]])
elif n_rows == 1: axes = axes.reshape(1, -1)
elif n_cols == 1: axes = axes.reshape(-1, 1)
for i, rx in enumerate(curr_rx):
r, c = i // n_cols, i % n_cols
sc.pl.spatial(metabolic_adata, img_key=img_key, color=[rx], size=spot_size, cmap='jet', show=False, ax=axes[r,c])
display_title = rx
if 'rxn_full_names' in metabolic_adata.var.columns and rx in metabolic_adata.var_names:
display_title = metabolic_adata.var.loc[rx, 'rxn_full_names']
wrapper = textwrap.TextWrapper(width=40)
axes[r,c].set_title(wrapper.fill(text=display_title), fontsize=10)
axes[r,c].axis('off')
for j in range(len(curr_rx), n_rows*n_cols): axes[j//n_cols, j%n_cols].axis('off')
plt.tight_layout()
rx_names_list = []
for rx in curr_rx:
if 'rxn_full_names' in metabolic_adata.var.columns and rx in metabolic_adata.var_names:
rx_names_list.append(metabolic_adata.var.loc[rx, 'rxn_full_names'])
else:
rx_names_list.append(rx)
rx_names_str = ", ".join(rx_names_list)
display_plot_with_download(
fig,
f"spatial_flux_p{st.session_state.spatial_flux_page}",
help_text=f"These maps show the spatial distribution of flux for: **{rx_names_str}**, allowing visualization of where specific metabolic processes are active."
)
plt.close(fig)
if pages > 1:
cx1, cx2, cx3 = st.columns([1,2,1])
if cx1.button("Previous Page", key="sf_prev"): st.session_state.spatial_flux_page -= 1; st.rerun()
cx2.markdown(f"<center>Reaction Page {st.session_state.spatial_flux_page} of {pages}</center>", unsafe_allow_html=True)
if cx3.button("Next Page", key="sf_next"): st.session_state.spatial_flux_page += 1; st.rerun()
except Exception as e:
st.error(f"Error: {e}")
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