Making a time dimention, not a time variable

[mccabete]

Hello geocube community.

Is there a way to make a geocube with a time dimension, like the x and y dimension? In most of the examples I've seen, it becomes a variable. In our use case, it would make more sense as a dimension. Here is an example of what I mean:

# Make example GeoPandas Dataframe
example = pd.DataFrame({"t":["2020-05-18 00:00:00", "2020-05-17 12:00:00"], 
                        "geometry":["POLYGON ((-112.01965966172988 33.847162226963476, -112.00119569986678 33.84892867068211, -111.99220894960493 33.85064041482308, -111.98165671264799 33.85853720177232, -111.98090089514638 33.86136925108656, -111.98084667720971 33.86153353095624, -111.98077318451573 33.861692682221054, -111.98068115524806 33.861845106063384, -111.98057151382108 33.861989271238706, -111.98044536159668 33.86212372945998, -111.98030396582294 33.862247129948436, -111.98014874690512 33.86235823300532, -111.9799812641373 33.86245592246789, -111.97980320003761 33.86253921692392, -111.97961634344459 33.86260727957326, -111.97942257154467 33.86265942663587, -111.97922383101103 33.862695134222676, -111.979022118444 33.8627140436, -111.9788194603091 33.86271596479392, -111.97861789257489 33.862700878499865, -111.97841944025508 33.862668936276265, -111.9782260970607 33.86262045902218, -111.97803980536713 33.86255593375272, -111.97786243669712 33.862476008705634, -111.97769577291626 33.86238148682763, -111.97754148832988 33.862273317706745, -111.97740113286132 33.86215258803082, -111.97727611648084 33.862020510668735, -111.97373615176265 33.85786245509311, -111.97362386842568 33.85771462728812, -111.97353071537296 33.85755758631497, -111.97345770264471 33.85739303512709, -111.973405621863 33.85722275810757, -111.97337503765226 33.85704860171882, -111.97336628152198 33.856872454477845, -111.97337944827761 33.85669622647708, -111.97341439499897 33.85652182867157, -111.97430647628607 33.853149841562804, -111.97436039141292 33.85298531470524, -111.97443362940321 33.85282589834573, -111.97452545292624 33.85267319716951, -111.97463493758349 33.85252874825689, -111.97476098121709 33.852394005612474, -111.9809330200267 33.846428090726945, -111.98108935286812 33.8462926144336, -111.98126229893415 33.84617218804351, -111.98144974961481 33.846068279771764, -111.99202463358378 33.84087638268918, -111.99219152745361 33.840802815368974, -111.99236560323162 33.84074241949829, -111.99254536450483 33.84069571425012, -111.99272926599635 33.84066310110882, -111.9929157268468 33.84064486041984, -111.99310314420143 33.84064114898034, -111.99328990698596 33.84065199869128, -111.99347440975323 33.840677316284136, -111.99785191550515 33.841455052144006, -112.0019486635434 33.84217757203369, -112.02027335843965 33.84146569842612, -112.0231617484476 33.83911429314538, -112.0197137461063 33.841921248666154, -112.01958616010131 33.84203490561246, -112.01947131941243 33.84215769938807, -112.01937018399823 33.84228860362591, -112.01772914692854 33.84463857877042, -112.01763551078844 33.84478920342574, -112.01756008740007 33.844946666585685, -112.01750362567942 33.84510940495811, -112.01746668630135 33.845275802868045, -112.01744963612865 33.845444208297415, -112.01745264456513 33.84561294928634, -112.01747568186848 33.84578035053225, -112.01751851944084 33.845944750023016, -112.01758073209334 33.846104515538556, -112.01766170226318 33.84625806085655, -112.01776062614103 33.8464038615029, -112.01787652164828 33.846540469888204, -112.01800823818493 33.84666652968211, -112.0181544680518 33.84678078928115, -112.01831375943308 33.846882114237204, -112.01848453081091 33.84696949852273, -112.01866508666866 33.84704207452076, -112.01885363432675 33.84709912164107, -112.01904830174418 33.847140073475366, -112.01924715610849 33.84716452342256, -112.01944822302957 33.84717222872599, -112.0196495061468 33.84716311288454, -112.0253336236241 33.84666709843979, -112.01965966172988 33.847162226963476))",
                                   "POLYGON ((-112.0231617484476 33.83911429314538, -112.0233095561006 33.839004945085186, -112.02346907396296 33.83890793190307, -112.0236388069369 33.83882416283437, -112.02381716420149 33.838754422982966, -112.02400247412116 33.838699365964885, -112.02419299991094 33.83865950778337, -112.02438695591167 33.838635221994075, -112.02458252432302 33.83862673620458, -112.02477787223711 33.83863412994172, -112.02497116881376 33.83865733390674, -112.02516060243622 33.838696130624605, -112.02534439768682 33.838750156481915, -112.02552083198373 33.83881890513451, -112.02568825172285 33.838901732251756, -112.02584508777377 33.83899786155473, -112.02598987018457 33.839106392090564, -112.02612124195801 33.83922630667464, -112.02623797176946 33.83935648142318, -112.02633896550823 33.839495696284885, -112.02642327653327 33.83964264647445, -112.02649011454767 33.83979595470039, -112.0265388530084 33.83995418407222, -112.02656903500198 33.84011585156723, -112.02716414924478 33.84477683563698, -112.02717553631622 33.844944513737545, -112.02716702052754 33.845112560973405, -112.02713868559533 33.84527932284504, -112.02709081036748 33.84544315749682, -112.02702386608246 33.84560245188245, -112.02693851173434 33.84575563764703, -112.02683558758879 33.84590120656914, -112.02671610691351 33.84603772541193, -112.02658124600497 33.84616385003521, -112.02643233260876 33.84627833863162, -112.0262708328484 33.84638006395438, -112.02609833679031 33.84646802441826, -112.02591654278788 33.84654135396245, -112.02572724075824 33.84659933057938, -112.02553229455705 33.846641383424696, -112.0253336236241 33.84666709843979, -112.0196495061468 33.84716311288454, -112.01944822302957 33.84717222872599, -112.01924715610849 33.84716452342256, -112.01904830174418 33.847140073475366, -112.01885363432675 33.84709912164107, -112.01866508666866 33.84704207452076, -112.01848453081091 33.84696949852273, -112.01831375943308 33.846882114237204, -112.0181544680518 33.84678078928115, -112.01800823818493 33.84666652968211, -112.01787652164828 33.846540469888204, -112.01776062614103 33.8464038615029, -112.01766170226318 33.84625806085655, -112.01758073209334 33.846104515538556, -112.01751851944084 33.845944750023016, -112.01747568186848 33.84578035053225, -112.01745264456513 33.84561294928634, -112.01744963612865 33.845444208297415, -112.01746668630135 33.845275802868045, -112.01750362567942 33.84510940495811, -112.01756008740007 33.844946666585685, -112.01763551078844 33.84478920342574, -112.01772914692854 33.84463857877042, -112.01937018399823 33.84228860362591, -112.01947131941243 33.84215769938807, -112.01958616010131 33.84203490561246, -112.0197137461063 33.841921248666154, -112.0231617484476 33.83911429314538))"
                                   ],
                        "frp_max":[5.46, 23.87]
                       })
example.geometry = example.geometry.apply(wkt.loads)
example = gpd.GeoDataFrame(example, crs = "EPSG:4326")

# Make it into a geocube

cube = make_geocube(vector_data=example, 
                    measurements = ['frp_max', "t"], 
                    datetime_measurements = ['t'],
                    resolution = (-0.01, 0.01),
                    fill=0)

In our case, the polygons represent "heat was detected here". If we plot "frp_max", we get a plot that makes sense. 0 frp was detected @ some pixels, and higher values were detected at others.

cube["frp_max"].plot()

But when you plot the time variable, the zeros for "no data" are also assigned to t, so you get "no data" values for some pixels. This means that some pixels have a "time" of 0, or whatever is the "no data' filler.

Ideally, each pixel would have a time dimension, like it has an x and y dimension. That way, we could keep track of when areas in-between our polygons had no detentions. Thanks for your thoughts!

[snowman2]

If you add group_by="t" in the make_geocube function, I think that should do what you want.

[mccabete]

Thanks @snowman2 that did exactly what I wanted. I should have guessed, because I saw that line in this example, I just for some reason thought it was specific to the example.