geoprior.scripts.plot_geo_cumulative#

Plot cumulative subsidence maps on satellite basemap + optional hotspots.

Layout#

The figure uses two rows by N columns, with Nansha on top and Zhongshan on the bottom. The first columns show 2022 observed and predicted cumulative maps, followed by forecast cumulative maps for later years.

Background and overlays#

Background: Esri.WorldImagery satellite tiles via contextily.
Foreground: PS points colored by cumulative subsidence (mm) since a baseline year, default 2020.
Optional overlay: hotspot points from the Fig. 6 hotspot CSV.

Cumulative definition#

This script always plots cumulative since start_year, but the input CSV can be:

--subsidence-kind=cumulative (default): values are already cumulative, so the script rebases them at the first year greater than or equal to start_year using cum(t) = value(t) - value(first).
--subsidence-kind=increment or rate: values are yearly increments, so the script accumulates them using cum(t) = sum_y increment(y).

CRS handling (lat/lon vs UTM)#

coord_x/coord_y may be:

lon/lat (degrees), treated as EPSG:4326
UTM (meters), treated as EPSG:<utm_epsg> with default 32649

Auto-detection:

if abs(x) <= 180 and abs(y) <= 90, treat the coordinates as lon/lat
otherwise, treat them as UTM

You can override using –coords-mode and –utm-epsg.

Outputs#

Saves both PNG and PDF using –out (resolved under scripts/figs if relative).

Notes

Code style targets black+ruff with line length 62.

Functions

`build_city_panels`(val_df, fut_df, *, ...[, ...])
`build_parser`(*[, prog])
`clip_bounds`(arrays, *, clip)
`draw_city_row`(*, axes, row_idx, city, ...)
`infer_xy_crs`(df, *, mode, utm_epsg)
`load_future_csv`(path)
`load_val_csv`(path)
`main`(*[, prog])
`parse_args`([argv, prog])
`plot_geo_cumulative_main`([argv, prog])
`to_web_mercator`(df, *, src_crs)

geoprior.scripts.plot_geo_cumulative.build_parser(*, prog=None)[source]#

Parameters:: prog (str | None)
Return type:: ArgumentParser

geoprior.scripts.plot_geo_cumulative.parse_args(argv=None, *, prog=None)[source]#

Parameters:

argv (list[str] | None)
prog (str | None)

Return type:

Namespace

geoprior.scripts.plot_geo_cumulative.load_val_csv(path)[source]#

Parameters:: path (str)
Return type:: DataFrame

geoprior.scripts.plot_geo_cumulative.load_future_csv(path)[source]#

Parameters:: path (str)
Return type:: DataFrame

geoprior.scripts.plot_geo_cumulative.infer_xy_crs(df, *, mode, utm_epsg)[source]#

Parameters:

df (DataFrame)
mode (str)
utm_epsg (int)

Return type:

str

geoprior.scripts.plot_geo_cumulative.to_web_mercator(df, *, src_crs)[source]#

Parameters:

df (DataFrame)
src_crs (str)

Return type:

GeoDataFrame

geoprior.scripts.plot_geo_cumulative.build_city_panels(val_df, fut_df, *, start_year, year_val, years_forecast, subs_kind='cumulative')[source]#

Parameters:

val_df (DataFrame)
fut_df (DataFrame)
start_year (int)
year_val (int)
years_forecast (list[int])
subs_kind (str)

Return type:

dict[str, DataFrame]

geoprior.scripts.plot_geo_cumulative.clip_bounds(arrays, *, clip)[source]#

Parameters:

arrays (list[ndarray])
clip (float)

Return type:

tuple[float, float]

geoprior.scripts.plot_geo_cumulative.draw_city_row(*, axes, row_idx, city, panels_3857, col_keys, cmap, cum_tag, vmin, vmax, args, show_panel_titles, hotspots)[source]#

Parameters:

axes (ndarray)
row_idx (int)
city (str)
panels_3857 (dict[str, GeoDataFrame])
col_keys (list[str])
cmap (Colormap)
cum_tag (str)
vmin (float)
vmax (float)
args (Namespace)
show_panel_titles (bool)
hotspots (DataFrame | None)

Return type:

None

geoprior.scripts.plot_geo_cumulative.plot_geo_cumulative_main(argv=None, *, prog=None)[source]#

Parameters:

argv (list[str] | None)
prog (str | None)

Return type:

None

geoprior.scripts.plot_geo_cumulative.main(*, prog=None)[source]#

Parameters:: prog (str | None)
Return type:: None