def main(file_name, jobs):
in1out2 = NMFunction(n=1, m=2)
in1out2.__metatype__ = "ADAPTER"
in1out2.set_parameter("function", datasets)
abs_reader = HigherOrderComponent(multiple_outputs_reader(m=3))
df_reader = HigherOrderComponent(multiple_outputs_reader(m=2))
feature_segmentation = ThicknessFeatureSegmentation()
feature_segmentation_out = NMFunction(m=3)
log_ratio = NMFunction(n=2)
log_ratio.set_parameter(
"function",
log_function)
average_abs = average()
average_df = average()
average_r = average()
network = {
in1out2: {
abs_reader: ("out", "in"),
df_reader: ("out1", "in"),
},
abs_reader: {
average_abs: ("out", "in"),
feature_segmentation: ("out1", "in"),
log_ratio: ("out2", "in1")
},
df_reader: {
log_ratio: ("out", "in"),
average_df: ("out1", "in")
},
feature_segmentation: {
feature_segmentation_out: ("out", "in")
},
log_ratio: {
average_r: ("out", "in")
},
feature_segmentation_out: {
average_abs: ("out", "in1"),
average_df: ("out1", "in1"),
average_r: ("out2", "in1")
},
}
pipeline = pypes.pipeline.Dataflow(network, n=jobs)
packet = pypes.packet.Packet()
packet.set("file_name", file_name)
pipeline.send(packet)
pipeline.close()
def average():
"calculate weighted average"
average = NMFunction(n=2)
average.set_parameter(
"function", average_function)
return average