TRK_StartTimestamp takes a track column and returns a datetime column that represents the first instant of each input track.
Tracks are linestrings that represent the change in an entity's location over time. Each vertex in the linestring has a timestamp (stored as the M-value) and the vertices are ordered sequentially.
For more information on using tracks in GeoAnalytics for Microsoft Fabric, see the core concept topic on tracks.
| Function | Syntax |
|---|---|
| Python | start |
| SQL | TRK |
| Scala | start |
For more details, go to the GeoAnalytics for Microsoft Fabric API reference for start_timestamp.
Examples
from geoanalytics_fabric.tracks import functions as TRK
from geoanalytics_fabric.sql import functions as ST
data = [
("LINESTRING M (-117.27 34.05 1633455010, -117.22 33.91 1633456062, -116.96 33.64 1633457132)",),
("LINESTRING M (-116.89 33.96 1633575895, -116.71 34.01 1633576982, -116.66 34.08 1633577061)",),
("LINESTRING M (-116.24 33.88 1633575234, -116.33 34.02 1633576336)",)
]
df = spark.createDataFrame(data, ["wkt",]) \
.withColumn("track", ST.line_from_text("wkt", srid=4326))
df.select(TRK.start_timestamp("track").alias("start_timestamp")).show()Result
+-------------------+
| start_timestamp|
+-------------------+
|2021-10-05 10:30:10|
|2021-10-06 20:04:55|
|2021-10-06 19:53:54|
+-------------------+Version table
| Release | Notes |
|---|---|
1.0.0-beta | Python, SQL, and Scala functions introduced |