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Topic: Midi Note Mapping (Read 13617 times) 1.I have tried to use the MIDI controller software several times to re-map the midi note numbers assigned to the Spark pads. I go through the process, send to Spark controller. Then nothing changes when trying to record and playback in Cubase 6 on a Mac.For Example: The Midi Note numbers record in from Spark C-2 (Kick), but Spark only receives the Kick on C3.I have mostly used the General Midi mapping with Kick being C1, so I would like to get Spark to use a similar template.Does anyone know how to achieve this in Spark? (re-mapping in the Midi Controller software has no affect)2. Also Pads 1-8 on Spark seam to output the same note numbers as Pads 9-16, which renders Pads 9-16 useless and only allows me to use 8 pads at a time. All 16 Pads seam to receive different note numbers which is good. Any assistance with these challenges is appreciated! Logged If you want to make Spark (software) to react to general MIDI mapping, go in Preferences view and change the "Choose MIDI Drum map model for Pads" to "General MIDI".It should do the job. If you want to make Spark Controller send GM mapping messages, you have to use MIDI Control Center and create a template.When you edit Spark controller message in MIDI Control Center, this change only messages sent in MIDI Controller Mode but not when in Spark Controller Mode. As you noticed, when Spark is in MIDI Control Mode, the 1-8/9-16 button has no effect. We will try to improve this in a future update but I can not tell you when it will be done.Kevin Logged KevinSoft Arturia just curious if something happened there regarding the "1-8/9-16 button in controller mode"? i would like to use the controller also as DAW controller..which works fine...just 8 track limit :-(.. would be nice to have 16 or even 24 ;-) Logged Not possible unfortunately due to lack of memory inside the controller. Logged KevinSoft Arturia thanks for the fast answer.. well, if it's not possible, it's not possible ;-) Logged Funny, I just mentioned the 1-8/9-16 function in a different thread...Kevin, I think you mean that the addition of 17-24 is not possible, or did you find that even switching 1-8/9-16 is not possible? Logged 1-8/9-16 is not possible Logged KevinSoft Arturia

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A pandas user-defined function (UDF)—also known as vectorized UDF—is a user-defined function that usesApache Arrow to transfer data and pandas to work with the data. pandas UDFs allowvectorized operations that can increase performance up to 100x compared to row-at-a-time Python UDFs.For background information, see the blog postNew Pandas UDFs and Python Type Hints in the Upcoming Release of Apache Spark 3.0.You define a pandas UDF using the keyword pandas_udf as a decorator and wrap the function with a Python type hint.This article describes the different types of pandas UDFs and shows how to use pandas UDFs with type hints.Series to Series UDF​You use a Series to Series pandas UDF to vectorize scalar operations.You can use them with APIs such as select and withColumn.The Python function should take a pandas Series as an input and return apandas Series of the same length, and you should specify these in the Pythontype hints. Spark runs a pandas UDF by splitting columns into batches, calling the functionfor each batch as a subset of the data, then concatenating the results.The following example shows how to create a pandas UDF that computes the product of 2 columns.Pythonimport pandas as pdfrom pyspark.sql.functions import col, pandas_udffrom pyspark.sql.types import LongType# Declare the function and create the UDFdef multiply_func(a: pd.Series, b: pd.Series) -> pd.Series: return a * bmultiply = pandas_udf(multiply_func, returnType=LongType())# The function for a pandas_udf should be able to execute with local pandas datax = pd.Series([1, 2, 3])print(multiply_func(x, x))# 0 1# 1 4# 2 9# dtype: int64# Create a Spark DataFrame, 'spark' is an existing SparkSessiondf = spark.createDataFrame(pd.DataFrame(x, columns=["x"]))# Execute function as a Spark vectorized UDFdf.select(multiply(col("x"), col("x"))).show()# +-------------------+# |multiply_func(x, x)|# +-------------------+# | 1|# | 4|# | 9|# +-------------------+Iterator of Series to Iterator of Series UDF​An iterator UDF is the same as a scalar pandas UDF except:The Python functionTakes an

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