There are a couple of setup steps necessary anytime you want to work with RasterFrames. the first is to import the API symbols into scope:
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| def set_dims(parts): | |
| assert(len(parts) is 2, "Expected dimensions specification to have exactly two components") | |
| assert(all([isinstance(p, int) and p > 0 for p in parts]), | |
| "Expected all components in dimensions to be positive integers") | |
| options.update({ | |
| "imageWidth": parts[0], | |
| "imageHeight": parts[1] | |
| }) | |
| if raster_dimensions is not None: |
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| # Tested with Spark 2.2.1, Python 3.6.4 | |
| # Even though we've used `--py-files` on the command line, | |
| # unintuitively this still seems necessary to import pyrasterframes | |
| SparkContext.addPyFile(spark.sparkContext, 'pyrasterframes.zip') | |
| from pyrasterframes import * | |
| spark.withRasterFrames() |
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| swagger: '2.0' | |
| info: | |
| title: Asset Catalog | |
| version: 0.5.1 | |
| description: >- | |
| This is a derivation of the <a href="https://raw.githubusercontent.com/radiantearth/stac-spec/master/api-spec/STAC-standalone-swagger2.yaml">STAC API Specification</a>. | |
| This is an OpenAPI definition of the core SpatioTemporal Asset Catalog API | |
| specification. Any service that implements this endpoint to allow search of | |
| spatiotemporal assets can be considered a STAC API. The endpoint is also | |
| available as an OpenAPI fragment that can be integrated with other OpenAPI |
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| 18/09/07 16:00:12 DEBUG RasterRef$: Fetching Extent(369450.0, 3353100.0, 371763.3, 3355458.3) from HttpGeoTiffRasterSource(https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/149/039/LC08_L1TP_149039_20170411_20170415_01_T1/LC08_L1TP_149039_20170411_20170415_01_T1_B4.TIF) | |
| ReadMonitor(reads=0, total=45876) | |
| +-----------+------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | |
| |spatial_key|bounds |src |
We can make this file beautiful and searchable if this error is corrected: It looks like row 2 should actually have 59 columns, instead of 3 in line 1.
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| PANCHROMATIC_LINES,NADIR_OFFNADIR,sunAzimuth,REFLECTIVE_SAMPLES,upperLeftCornerLongitude,cloudCover,MAP_PROJECTION_L1,cartURL,sunElevation,path,BPF_NAME_TIRS,THERMAL_LINES,GROUND_CONTROL_POINTS_MODEL,row,imageQuality1,REFLECTIVE_LINES,ELLIPSOID,GEOMETRIC_RMSE_MODEL,browseURL,browseAvailable,dayOrNight,CPF_NAME,DATA_TYPE_L1,THERMAL_SAMPLES,upperRightCornerLatitude,lowerLeftCornerLatitude,sceneStartTime,dateUpdated,sensor,PANCHROMATIC_SAMPLES,GROUND_CONTROL_POINTS_VERSION,LANDSAT_PRODUCT_ID,acquisitionDate,upperRightCornerLongitude,PROCESSING_SOFTWARE_VERSION,GRID_CELL_SIZE_REFLECTIVE,lowerRightCornerLongitude,lowerRightCornerLatitude,sceneCenterLongitude,COLLECTION_CATEGORY,GRID_CELL_SIZE_PANCHROMATIC,BPF_NAME_OLI,sceneCenterLatitude,CLOUD_COVER_LAND,lowerLeftCornerLongitude,GEOMETRIC_RMSE_MODEL_X,GEOMETRIC_RMSE_MODEL_Y,sceneStopTime,upperLeftCornerLatitude,UTM_ZONE,DATE_L1_GENERATED,GRID_CELL_SIZE_THERMAL,DATUM,COLLECTION_NUMBER,sceneID,RLUT_FILE_NAME,TIRS_SSM_MODEL,ROLL_ANGLE,receivingStation | |
| 16421,NADIR,162 |
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| [ | |
| { | |
| "jmhVersion" : "1.20", | |
| "benchmark" : "org.locationtech.geomesa.spark.jts.WKBNumPointsBench.deserializeNumPoints", | |
| "mode" : "thrpt", | |
| "threads" : 1, | |
| "forks" : 1, | |
| "jvm" : "/Library/Java/JavaVirtualMachines/jdk1.8.0_161.jdk/Contents/Home/jre/bin/java", | |
| "jvmArgs" : [ | |
| ], |
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| import geotrellis.spark.{SpatialComponent, TemporalComponent} | |
| object Test { | |
| class Foo { | |
| // ... | |
| } | |
| object Foo { | |
| implicit val spatialComponent: SpatialComponent[Foo] = ??? | |
| } |