From 28e984d1ce84c92b858feb86578ed47438dd4ea9 Mon Sep 17 00:00:00 2001 From: Alex Gardner <32276930+alex-s-gardner@users.noreply.github.com> Date: Mon, 4 Mar 2024 15:03:33 -0800 Subject: [PATCH] Update README.md I've updated the ReadMe to make it easier for a user to fun as written. --- README.md | 61 ++++++++++++++++++++++++------------------------------- 1 file changed, 26 insertions(+), 35 deletions(-) diff --git a/README.md b/README.md index 02cfe3d..9709b8f 100644 --- a/README.md +++ b/README.md @@ -38,11 +38,11 @@ using Extents granules = search(:ICESat2, :ATL08) # Find only ATL03 granules in a part of Vietnam -vietnam = Extent(X = (102., 107.0), Y = (8.0, 12.0)) +vietnam = Extent(X=(102.0, 107.0), Y=(8.0, 12.0)) granules = search(:ICESat2, :ATL08; extent=vietnam, version=6) # Find GEDI granules in the same way -granules = search(:GEDI, :GEDI02_A) +granules = search(:GEDI, :GEDI02_A; extent=vietnam) # A granule is pretty simple granule = granules[1] @@ -54,54 +54,45 @@ granule.info # derived information from id # we provide a helper function, that creates/updates a ~/.netrc or ~/_netrc SpaceLiDAR.netrc!(username, password) # replace with your credentials -# Afterward you can download the dataset -fn = SpaceLiDAR.download!(granule) +# Afterward you can download the dataset. +# Note: download! updated granule url to local path +granule = SpaceLiDAR.download!(granule) # You can also load a granule from disk -granule = granule(fn) +path2file = granule.url +granule = SpaceLiDAR.granule(path2file) # Or from a folder -local_granules = granules(folder) - -# Instantiate search results locally (useful for GEDI location indexing) -local_granules = instantiate(granules, folder) +(folder, fn) = splitdir(path2file) +local_granules = SpaceLiDAR.granules(folder) ``` Derive points ```julia using DataFrames fn = "GEDI02_A_2019242104318_O04046_01_T02343_02_003_02_V002.h5" -g = SpaceLiDAR.granule(fn) -df = DataFrame(g) -149680×15 DataFrame - Row │ longitude latitude height height_error datetime intensity sensitivity surface quality nmo ⋯ - │ Float64 Float64 Float32 Float32 DateTime Float32 Float32 Bool Bool UIn ⋯ -────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── - 1 │ 153.855 -47.2772 -13.3536 0.307976 2019-08-30T10:48:21.047 393.969 -0.0671094 true false ⋯ - 2 │ 153.855 -47.2769 -11.2522 0.307978 2019-08-30T10:48:21.055 797.26 0.533529 true true - 3 │ 153.856 -47.2767 -13.775 0.307981 2019-08-30T10:48:21.063 1010.39 0.695938 true true - 4 │ 153.857 -47.2765 -11.729 0.307983 2019-08-30T10:48:21.071 852.614 0.544849 true true - 5 │ 153.857 -47.2763 -13.2443 0.307985 2019-08-30T10:48:21.080 980.66 0.620767 true true ⋯ - 6 │ 153.858 -47.2761 -12.1813 0.307987 2019-08-30T10:48:21.088 937.441 0.620531 true true - 7 │ 153.859 -47.2758 -11.9011 0.30799 2019-08-30T10:48:21.096 1235.02 0.73815 true true - 8 │ 153.859 -47.2756 -12.3796 0.307992 2019-08-30T10:48:21.104 854.127 0.545655 true true +granule = SpaceLiDAR.granule(fn) + +df = DataFrame(granule) +760156×15 DataFrame + Row │ longitude latitude height height_error datetime intensity sensitivity surface quality nmodes track strong_beam classification sun_angle height_reference + │ Float64 Float64 Float32 Float32 DateTime Float32 Float32 Bool Bool UInt8 String Bool String Float32 Float32 +────────┼───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── + 1 │ 26.6923 51.823 169.045 0.313182 2019-04-18T10:22:23.996 -857.388 1.38006 true false 1 BEAM0000 false ground 49.0315 169.752 + 2 │ 26.7006 51.823 165.783 0.31319 2019-04-18T10:22:24.078 853.56 0.694586 true false 1 BEAM0000 false ground 49.0312 167.354 + 3 │ 26.7023 51.823 162.871 0.313192 2019-04-18T10:22:24.095 110.071 -0.480232 true false 1 BEAM0000 false ground 49.0311 164.785 + ⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ + 760155 │ 110.661 -0.194184 171.157 0.258848 2019-04-18T10:45:33.900 7702.96 0.945006 true true 2 BEAM1011 true ground -1.94442 176.333 + 760156 │ 110.662 -0.195451 167.176 0.258852 2019-04-18T10:45:33.925 9595.64 0.981322 true true 2 BEAM1011 true ground -1.94564 173.691 ``` Derive linestrings ```julia using DataFrames -fn = "ATL03_20181110072251_06520101_003_01.h5" -g = SpaceLiDAR.granule(fn) -tlines = DataFrame(SpaceLiDAR.lines(g, step=10000)) -Table with 4 columns and 6 rows: - geom sun_angle track datetime - ┌─────────────────────────────────────────────────────────────────────────── - 1 │ wkbLineString25D geometry 38.3864 gt1l_weak 2018-11-10T07:28:01.688 - 2 │ wkbLineString25D geometry 38.375 gt1r_strong 2018-11-10T07:28:02.266 - 3 │ wkbLineString25D geometry 38.2487 gt2l_weak 2018-11-10T07:28:04.474 - 4 │ wkbLineString25D geometry 38.1424 gt2r_strong 2018-11-10T07:28:07.374 - 5 │ wkbLineString25D geometry 38.2016 gt3l_weak 2018-11-10T07:28:05.051 - 6 │ wkbLineString25D geometry 38.1611 gt3r_strong 2018-11-10T07:28:06.344 +fn = "GEDI02_A_2019108093620_O01965_03_T05338_02_003_01_V002.h5" +granule = SpaceLiDAR.granule(fn) +tlines = DataFrame.(SpaceLiDAR.lines(granule; step=10000)) + SpaceLiDAR.GDF.write("lines.gpkg", tlines) ```