Metadata cleanup

examples/dlt_tempo.py

@@ -26,7 +26,7 @@ def ts_bronze():
 @dlt.expect_or_drop("User_check","User in ('a','c','i')")
 def ts_ft():
   phone_accel_df = dlt.read("ts_bronze")
-  phone_accel_tsdf = TSDF(phone_accel_df, ts_col="event_ts", partition_cols = ["User"])
+  phone_accel_tsdf = TSDF(phone_accel_df, ts_col="event_ts")
   ts_ft_df = phone_accel_tsdf.fourier_transform(timestep=1, valueCol="x").df
   return ts_ft_df
 

examples/financial_services_quickstart.py

@@ -92,8 +92,8 @@
 # DBTITLE 1,Define TSDF Time Series Data Structure
 from tempo import *
 
-trades_tsdf = TSDF(trades_df, partition_cols = ['date', 'symbol'], ts_col = 'event_ts')
-quotes_tsdf = TSDF(quotes_df, partition_cols = ['date', 'symbol'], ts_col = 'event_ts')
+trades_tsdf = TSDF(trades_df, ts_col='event_ts')
+quotes_tsdf = TSDF(quotes_df, ts_col='event_ts')
 
 # COMMAND ----------
 
@@ -178,7 +178,7 @@
 from tempo import *
 from pyspark.sql.functions import *
 
-minute_bars = TSDF(spark.table("time_test"), partition_cols=['ticker'], ts_col="ts").calc_bars(freq = '1 minute', func= 'ceil')
+minute_bars = TSDF(spark.table("time_test"), ts_col="ts").calc_bars(freq ='1 minute', func='ceil')
 
 display(minute_bars)
 

python/README.md

@@ -51,7 +51,7 @@ phone_accel_df = spark.read.format("csv").option("header", "true").load("dbfs:/h
 
 from tempo import * 
 
-phone_accel_tsdf = TSDF(phone_accel_df, ts_col="event_ts", partition_cols = ["User"])
+phone_accel_tsdf = TSDF(phone_accel_df, ts_col="event_ts", series_ids = ["User"])
 
 display(phone_accel_tsdf)
 ```
@@ -65,7 +65,7 @@ Note: You can upsample any missing values by using an option in the resample int
 
 ```python
 # ts_col = timestamp column on which to sort fact and source table
-# partition_cols - columns to use for partitioning the TSDF into more granular time series for windowing and sorting
+# series_ids - columns to use for partitioning the TSDF into more granular time series for windowing and sorting
 
 resampled_sdf = phone_accel_tsdf.resample(freq='min', func='floor')
 resampled_pdf = resampled_sdf.df.filter(col('event_ts').cast("date") == "2015-02-23").toPandas()
@@ -97,7 +97,7 @@ from pyspark.sql.functions import *
 
 watch_accel_df = spark.read.format("csv").option("header", "true").load("dbfs:/home/tempo/Watch_accelerometer").withColumn("event_ts", (col("Arrival_Time").cast("double")/1000).cast("timestamp")).withColumn("x", col("x").cast("double")).withColumn("y", col("y").cast("double")).withColumn("z", col("z").cast("double")).withColumn("event_ts_dbl", col("event_ts").cast("double"))
 
-watch_accel_tsdf = TSDF(watch_accel_df, ts_col="event_ts", partition_cols = ["User"])
+watch_accel_tsdf = TSDF(watch_accel_df, ts_col="event_ts", series_ids = ["User"])
 
 # Applying AS OF join to TSDF datasets
 joined_df = watch_accel_tsdf.asofJoin(phone_accel_tsdf, right_prefix="phone_accel")
@@ -107,12 +107,12 @@ display(joined_df)
 
 #### 3. Skew Join Optimized AS OF Join
 
-The purpose of the skew optimized as of join is to bucket each set of `partition_cols` to get the latest source record merged onto the fact table
+The purpose of the skew optimized as of join is to bucket each set of `series_ids` to get the latest source record merged onto the fact table
 
 Parameters: 
 
 ts_col = timestamp column for sorting 
-partition_cols = partition columns for defining granular time series for windowing and sorting
+series_ids = partition columns for defining granular time series for windowing and sorting
 tsPartitionVal = value to break up each partition into time brackets
 fraction = overlap fraction
 right_prefix = prefix used for source columns when merged into fact table
@@ -185,11 +185,10 @@ Valid columns data types for interpolation are: `["int", "bigint", "float", "dou
 ```python
 # Create instance of the TSDF class
 input_tsdf = TSDF(
-            input_df,
-            partition_cols=["partition_a", "partition_b"],
-            ts_col="event_ts",
-        )
-
+    input_df,
+    series_ids=["partition_a", "partition_b"],
+    ts_col="event_ts",
+)
 
 # What the following chain of operation does is:
 # 1. Aggregate all valid numeric columns using mean into 30 second intervals
@@ -205,32 +204,32 @@ interpolated_tsdf = input_tsdf.resample(freq="30 seconds", func="mean").interpol
 interpolated_tsdf = input_tsdf.interpolate(
     freq="30 seconds",
     func="mean",
-    target_cols= ["columnA","columnB"],
+    target_cols=["columnA", "columnB"],
     method="linear"
 
 )
 
 # Alternatively it's also possible to override default TSDF parameters.
-# e.g. partition_cols, ts_col a
+# e.g. series_ids, ts_col a
 interpolated_tsdf = input_tsdf.interpolate(
-    partition_cols=["partition_c"],
+    series_ids=["partition_c"],
     ts_col="other_event_ts"
-    freq="30 seconds",
-    func="mean",
-    target_cols= ["columnA","columnB"],
-    method="linear"
+freq = "30 seconds",
+       func = "mean",
+              target_cols = ["columnA", "columnB"],
+                            method = "linear"
 )
 
 # The show_interpolated flag can be set to `True` to show additional boolean columns 
 # for a given row that shows if a column has been interpolated.
 interpolated_tsdf = input_tsdf.interpolate(
-    partition_cols=["partition_c"],
+    series_ids=["partition_c"],
     ts_col="other_event_ts"
-    freq="30 seconds",
-    func="mean",
-    method="linear",
-    target_cols= ["columnA","columnB"],
-    show_interpolated=True,
+freq = "30 seconds",
+       func = "mean",
+              method = "linear",
+                       target_cols = ["columnA", "columnB"],
+                                     show_interpolated = True,
 )
 
 ```