| name | figure-generation |
| description | Generate, update, and modify publication figures for the multicellular compaction project. Handles jupytext .py/.ipynb pairing, minimalist science style, LaTeX sizing, SVG output, and compaction_analysis/compaction_plotting imports. Use when: editing figure scripts, adding panels, fixing axes/colors/legends, styling error bars, saving figures, or creating new figure scripts. |
Figure Generation
Critical Rules
- Edit
.py only, never .ipynb — jupytext syncs from .py. Scripts: scripts/fig*.py (and scripts/SI/ for supplementary); cells # %% / # %% [markdown], 5 blank lines between cells.
- Separation of concerns: Analysis returns data only (arrays/DataFrames), no
plt. Plot functions take ax and only draw; they never load data or do heavy analysis.
- API shape: High-level plots are
plot_*(ax, dataframes..., base_params, **options); they never create fig, ax. Caller owns fig, ax = plt.subplots(...), plt.savefig(...), then plt.show().
Setup Block
After imports in every figure script (__file__ is the script under scripts/):
import os, sys
import matplotlib.pyplot as plt
import minimalist
minimalist.use_style("science")
plt.rcParams.update({'figure.facecolor': 'none', 'axes.facecolor': 'none', 'savefig.facecolor': 'none'})
text_width = 510 / 72.27
fw, fw_2, fw_3, fw_4 = text_width, 0.5 * text_width, 0.33 * text_width, 0.25 * text_width
colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
repo_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
sys.path.insert(0, os.path.join(repo_root, 'scripts', 'src'))
data_dir = os.path.join(repo_root, 'results')
sim_data_dir = os.path.join(data_dir, 'data', 'simulation', 'simulation_analysis')
sim_frames_dir = os.path.join(data_dir, 'data', 'simulation', 'sim_frames')
exp_data_dir = os.path.join(data_dir, 'data', 'experiment')
plot_dir = os.path.join(data_dir, 'plots', 'publication')
Style (single source of truth)
- Sizing:
fw–fw_4 from setup; default aspect square; minimalist.figsize(fraction, aspect_ratio=) for non-square.
- Colors:
colors from setup (warm→cool palette). Colormaps: pride, inferno, minimalist. See STYLE.md.
- Error bars: marker + vertical line + caps; no filled bands.
- Legends:
ax.legend(labelcolor="linecolor") then minimalist.color_legend_text(ax).
- Layout:
constrained_layout=True; never tight_layout(). Axes limits/ticks encode physics (e.g. fixed cell-gap ranges for compaction); equal aspect for network/spatial plots.
Modules & data
from compaction_analysis import load_arrow_data, compaction_fit, compaction_derivative
from compaction_plotting import draw_matrix, draw_cell_edges, draw_cell_nodes, draw_alpha_shape_and_compaction, plot_network
- Data:
.arrow → load_arrow_data(path); processed → pd.read_csv(path).
draw_matrix(df, ax, df_initial=None, traction=None, linewidth=0.5) — traction=(lambda_m, cell_size) for orange edges.plot_network(df, ax, box_size, style="matrix_network", initial_df=None, traction=None, alpha_shape=1.0).
Saving
plt.savefig(os.path.join(plot_dir, 'fig_X_panel_name.svg'), bbox_inches='tight')
plt.show()
SVG by default; savefig before show().
References
