flowmpl 0.1.1

Matplotlib design system and flow diagram renderer for analytical publications

flowmpl

Matplotlib design system and flow diagram renderer for analytical publications.

Built for research notebooks that combine data visualization with process flow diagrams — the kind of work where you need both a consistent visual language across charts and clean auto-routed arrows between labeled boxes.

Install

pip install flowmpl           # core: matplotlib + numpy only
pip install flowmpl[charts]   # + pandas (annotated_series, stacked_bar, etc.)
pip install flowmpl[maps]     # + geopandas + requests (us_scatter_map)
pip install flowmpl[all]      # everything

Quick Start

from flowmpl import COLORS, FONTS, flow_diagram

fig = flow_diagram(
    nodes={
        "a": ("Announce", 1.0, 1.0, COLORS["accent"],    "#ffffff"),
        "b": ("Permit",   3.0, 1.0, COLORS["neutral"],   COLORS["text_dark"]),
        "c": ("Build",    5.0, 1.0, COLORS["positive"],  "#ffffff"),
    },
    edges=[
        {"src": "a", "dst": "b", "label": "12–18 mo"},
        {"src": "b", "dst": "c", "label": "24–36 mo"},
    ],
    title="From announcement to operation",
)
fig.savefig("pipeline.png", dpi=150, bbox_inches="tight")

Design System

flowmpl ships a complete design system extracted from a production research project:

Token	Description
COLORS	Semantic roles: accent, positive, negative, neutral, muted, reference, background, text_dark, text_light
CONTEXT	SWD gray — use for non-focus elements in gray+accent charts
FONTS	Font sizes for titles, labels, annotations, legends, captions
FIGSIZE	Named figure sizes: standard, wide, tall, map
CATEGORICAL	8-color Paul Tol colorblind-safe palette
FUEL_COLORS	Energy generation types: solar, wind, battery, gas_cc, gas_ct, nuclear, hydro, coal…
COMPANY_COLORS	Hyperscaler tickers: MSFT, AMZN, GOOGL, META, NVDA, ORCL, AAPL, TSLA

Chart Functions

All functions return matplotlib.Figure objects.

from flowmpl import (
    annotated_series,       # time series with annotations + fill
    multi_panel,            # multi-subplot from a single DataFrame
    stacked_bar,            # stacked bar for categorical breakdowns
    waterfall_chart,        # cost allocation / flow breakdowns
    horizontal_bar_ranking, # ranked horizontal bars with highlights
)

Helpers

from flowmpl import (
    focus_colors,    # SWD gray+accent pattern: focus items colored, rest gray
    legend_below,    # place legend below axes (works with bbox_inches='tight')
    annotate_point,  # annotate a data point with arrow + text
    reference_line,  # labeled horizontal or vertical reference line
    chart_title,     # subtle left-aligned insight title (for standalone PNGs)
)

focus_colors() example

colors = focus_colors(
    ["MSFT", "AMZN", "GOOGL", "META"],
    focus="AMZN",
    color_map=COMPANY_COLORS,
)
# → ['#c0c0c0', '#ff9900', '#c0c0c0', '#c0c0c0']

Flow Diagram

flow_diagram() renders labeled boxes connected by auto-routed arrows. It handles:

• Auto-routing: chooses straight or elbow connections based on source/destination geometry
• Face overrides: explicit exit/entry keys bypass the heuristic for complex layouts
• Auto-spacing: adjusts vertical spacing to prevent label overlap
• Face spreading: distributes multiple edges from the same face to avoid overlaps

Node format

nodes = {
    "key": (label, cx, cy, fill_color, text_color),
}

• cx, cy: center coordinates in abstract units (the renderer scales to fit)
• Sizes auto-scaled; override with node_width / node_height params

Edge format

edges = [
    {"src": "a", "dst": "b"},                           # basic
    {"src": "a", "dst": "b", "label": "2 years"},       # with label
    {"src": "a", "dst": "b", "dashed": True},           # dashed line
    {"src": "a", "dst": "b", "color": "#888"},          # custom color
    {"src": "a", "dst": "b", "exit": "top",   "entry": "left"},  # face override
    {"src": "a", "dst": "b", "exit": "bottom", "entry": "left"}, # force exit face
]

Routing heuristic

The auto-router uses the vector between node centers:

Condition	Route style
|vy| < 0.25 \* |vx|	Near-horizontal — straight line
|vx| < 0.25 \* |vy|	Near-vertical — straight line
|vy| < 0.75 \* |vx|	Primarily-horizontal — exit top/bottom, enter side
else	Primarily-vertical — exit side, enter top/bottom

Use explicit exit/entry overrides when the heuristic produces overlapping arrows from the same face (e.g., a root node with multiple connections at different angles).

T-layout example (face overrides)

fig = flow_diagram(
    nodes={
        "r": ("Root",   1.0, 1.5, COLORS["accent"],   "#ffffff"),
        "a": ("Top",    4.5, 3.0, COLORS["positive"], "#ffffff"),
        "m": ("Middle", 4.5, 1.5, COLORS["neutral"],  COLORS["text_dark"]),
        "b": ("Bottom", 4.5, 0.0, CONTEXT,            COLORS["text_dark"]),
    },
    edges=[
        {"src": "r", "dst": "a", "exit": "top",    "entry": "left"},
        {"src": "r", "dst": "m"},                           # straight, no override needed
        {"src": "r", "dst": "b", "exit": "bottom", "entry": "left"},
    ],
    title="Three destinations from one source",
)

Without the face overrides, all three arrows would exit from r's right face and overlap. With exit="top" and exit="bottom", each arrow leaves from a different face.

Map

from flowmpl import us_scatter_map
import matplotlib.patches as mpatches

fig = us_scatter_map(
    lats=[37.7, 40.7, 41.8],
    lons=[-122.4, -74.0, -87.6],
    colors=["#e74c3c", "#3498db", "#2ecc71"],
    sizes=[100, 150, 80],
    title="Three cities",
    legend_handles=[
        mpatches.Patch(color="#e74c3c", label="San Francisco"),
        mpatches.Patch(color="#3498db", label="New York"),
        mpatches.Patch(color="#2ecc71", label="Chicago"),
    ],
)

State boundary shapefiles are downloaded from the US Census Bureau on first use and cached in the package directory.

License

MIT