Resolutions

resolution is the HEALPix order, and it is the only grid parameter there is. Polypix accepts 0 through 29. When you are allocating or checking a dense map, polypix.cell_count(resolution) gives you the length.

Every resolution starts from the same 12 cells and splits each one into four. Cells are equal in area at every level, which is what makes the grid useful for counting, and why they change shape between the poles and the equator.

The same region on three grids. Each step up quadruples the cell count and roughly halves the cell size.

Reading the table

Every cell on the sphere covers exactly the same solid angle, but their shapes differ: a cell near a pole is not shaped like one on the equator, so no single edge length holds everywhere. The nominal cell size below is sqrt(cell area), which is the usual stand-in and matches what healpy.nside2resol() reports. Treat it as a typical size rather than as a bound on how wide any particular cell gets.

The Earth column is that same angle laid on a sphere of radius 6,371 km, the IUGG mean radius, so it is roughly the distance across a typical cell at ground level.

Resolution	nside	Cells	Nominal cell size	On Earth	Dense int64 map
0	1	12	58.63°	6,520 km	96 B
1	2	48	29.32°	3,260 km	384 B
2	4	192	14.66°	1,630 km	1.5 KiB
3	8	768	7.329°	815 km	6 KiB
4	16	3,072	3.665°	407.5 km	24 KiB
5	32	12,288	1.832°	203.7 km	96 KiB
6	64	49,152	54.97′	101.9 km	384 KiB
7	128	196,608	27.48′	50.93 km	1.5 MiB
8	256	786,432	13.74′	25.47 km	6 MiB
9	512	3,145,728	6.871′	12.73 km	24 MiB
10	1,024	12,582,912	3.435′	6.367 km	96 MiB
11	2,048	50,331,648	1.718′	3.183 km	384 MiB
12	4,096	201,326,592	51.53″	1.592 km	1.5 GiB
13	8,192	805,306,368	25.77″	795.9 m	6 GiB
14	16,384	3,221,225,472	12.88″	397.9 m	24 GiB
15	32,768	12,884,901,888	6.442″	199 m	96 GiB
16	65,536	51,539,607,552	3.221″	99.48 m	384 GiB
17	131,072	206,158,430,208	1.61″	49.74 m	1.5 TiB
18	262,144	824,633,720,832	805.2 mas	24.87 m	6 TiB
19	524,288	3,298,534,883,328	402.6 mas	12.44 m	24 TiB
20	1,048,576	13,194,139,533,312	201.3 mas	6.218 m	96 TiB
21	2,097,152	52,776,558,133,248	100.6 mas	3.109 m	384 TiB
22	4,194,304	211,106,232,532,992	50.32 mas	1.554 m	1.5 PiB
23	8,388,608	844,424,930,131,968	25.16 mas	777.2 mm	6 PiB
24	16,777,216	3,377,699,720,527,872	12.58 mas	388.6 mm	24 PiB
25	33,554,432	13,510,798,882,111,488	6.291 mas	194.3 mm	96 PiB
26	67,108,864	54,043,195,528,445,952	3.145 mas	97.15 mm	384 PiB
27	134,217,728	216,172,782,113,783,808	1.573 mas	48.57 mm	1.5 EiB
28	268,435,456	864,691,128,455,135,232	786.3 µas	24.29 mm	6 EiB
29	536,870,912	3,458,764,513,820,540,928	393.2 µas	12.14 mm	24 EiB

Picking one

Match the grid to the smallest feature you care about.

Some rough anchors:

• Resolutions 6 and 7, around 100 km and 50 km, suit constellation coverage and revisit studies. The case studies use 6 for communications and 7 for Earth observation.

• Resolutions 8 to 10, from 25 km down to 6 km, suit regional analysis and most sensor-footprint work.

• Resolution 12, around 1.6 km, is about where a dense global map stops being comfortable, since one int64 per cell is already 1.5 GiB.

• Above 13, dense global arrays are out of the question. The high resolutions are there for sparse work: pass candidate_cells= to restrict coverage to the cells you care about, or to Coverage.reduce() to query a short list. See Performance and memory.