min_duration=2, max_duration=20))
unstatic = Corpus(
"~/Cloud/Projects/ElectroMagnetic/outputs/classification/4_Split/0")
static = Corpus(
"~/Cloud/Projects/ElectroMagnetic/outputs/classification/4_Split/1")
output = "../../reaper/Convolutions/tuned"
analysis = Chain(source=(db + tuned + unstatic + static), folder=output)
kdtree = KDTree()
dr = UMAP(components=10, cache=1) # we need access to the original data
analysis.add(
# FluidMFCC(discard=True, numcoeffs=20, fftsettings=[4096, -1, -1], cache=1),
LibroCQT(cache=0),
Stats(numderivs=1, flatten=True, cache=1),
Standardise(cache=1),
dr,
kdtree)
if __name__ == "__main__":
analysis.run()
pinpoint = tuned.items[0] # single item
x = dr.output[pinpoint]
dist, ind = kdtree.model.query([x], k=200)
keys = [x for x in dr.output.keys()]
names = [keys[x] for x in ind[0]]
d = {"1": names}
write_json(analysis.folder / "nearest_files.json", d)
db = (Corpus("~/Cloud/Projects/DataBending/DataAudioUnique").duration(
min_duration=2, max_duration=20))
em = Corpus(
"~/Cloud/Projects/ElectroMagnetic/outputs/classification/4_Split/1")
output = "../../reaper/Convolutions/anchors"
analysis = Chain(source=(em + db), folder=output)
kdtree = KDTree()
dr = UMAP(components=10, cache=1) # we need access to the original data
analysis.add(
FluidMFCC(discard=True, numcoeffs=20, fftsettings=[4096, -1, -1], cache=1),
Stats(numderivs=1, flatten=True, cache=1), Standardise(cache=1), dr,
kdtree)
if __name__ == "__main__":
analysis.run()
tracks = {}
for anchor in anchors:
pos = 0
point = dr.output[anchor]
dist, ind = kdtree.model.query([point], k=25)
keys = [x for x in dr.output.keys()]
names = [keys[x] for x in ind[0]]
names.append(anchor)
for i in names:
dur = get_duration(i)