spotmap 0.1.17

Interactive epidemiological spot maps for India

SpotMap

Interactive epidemiological spot maps for India

SpotMap turns a CSV of case/control coordinates into a publication-ready interactive HTML map with:

• Dot density clustering (cases only)
• Spot map pins (cases and/or controls) with custom colours
• Automatic state/district boundary overlays (bundled data — no shapefile setup needed)
• Sidebar with mode toggle, colour pickers, pin size slider, and PNG/PDF export
• Smart auto-detection of latitude, longitude, and outcome columns

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Installation

pip install spotmap

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Quick start

Interactive (no coding required)

The easiest way — works great in Google Colab or a Jupyter notebook. Paste these two lines into a cell and run it:

!pip install spotmap
from spotmap import spotmap_run
spotmap_run()

SpotMap then walks you through a few simple prompts:

1. Upload your data file (in Colab an upload button appears; elsewhere it asks for the file path).
2. Confirm which columns hold latitude, longitude, and the case/control outcome (SpotMap pre-selects its best guess).
3. Pick which value means "case".

The finished map is shown right in the notebook cell — no need to hunt for a file — and is also saved as spotmap.html (pass a different name with spotmap_run("my_map.html")).

In a plain terminal, spotmap_run() reads your answers from the keyboard and saves an HTML file to open in your browser. For automated scripts, use the Python API below instead.

Python API

from spotmap import SpotMap

SpotMap("my_data.csv").build().save("map.html")

Command line

spotmap my_data.csv -o map.html

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CSV format

SpotMap auto-detects columns — no strict naming required.

Requirement	Details
Coordinates	Separate lat / lon columns or a combined "lat,lon" column
Outcome	A column named outcome, status, case_control, etc. with values like case / control

Example:

latitude,longitude,outcome
28.6,77.2,case
19.0,72.8,control
13.0,80.2,case

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Python API reference

SpotMap(
    csv_path,                       # required
    state_shp=None,                 # custom state boundary (shapefile/GeoPackage)
    district_shp=None,              # custom district boundary
    lat_col=None,                   # override auto-detection
    long_col=None,
    outcome_col=None,
    case_value=None,                # value that means "case" in outcome_col
    count_cutoff=2,                 # districts ≤ cutoff → district zoom
    margin_deg=1.0,                 # boundary crop padding
    cluster_color="#E85252",        # dot-density bubble colour
    case_color="#D55757",           # case pin colour
    control_color="#7676E7",        # control pin colour
)

Chain calls:

sm = SpotMap("data.csv", case_color="#FF0000").build()
sm.save("map.html")

# Access the raw Folium map for further customisation
folium_map = sm.map

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CLI reference

usage: spotmap [-h] [-o OUTPUT] [--state-shp STATE_SHP]
               [--district-shp DISTRICT_SHP] [--lat-col LAT_COL]
               [--lon-col LON_COL] [--outcome-col OUTCOME_COL]
               [--case-value CASE_VALUE] [--count-cutoff COUNT_CUTOFF]
               [--cluster-color CLUSTER_COLOR] [--case-color CASE_COLOR]
               [--control-color CONTROL_COLOR]
               csv

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License

MIT © Tharun Mallesan