import jinja2
"""
Give me a crude 3 cluster output of all of the 'static' files. Later on we'll find other files similar to these.
We can use both bits of information to help compose.
"""
output = "../../reaper/Convolutions/base_materials"
em = Corpus("~/Cloud/Projects/ElectroMagnetic/outputs/classification/4_Split/1")
analysis = Chain(
source = (em),
folder = output
)
dr = UMAP(components=10, cache=1)
clustering = AgglomerativeClustering(numclusters=3)
analysis.add(
FluidMFCC(discard=True, numcoeffs=20, fftsettings=[2048, -1, -1], cache=1),
Stats(numderivs=1, flatten=True, cache=1),
Standardise(cache=1),
dr,
clustering
)
if __name__ == "__main__":
analysis.run()
tracks = {}
for cluster, items in clustering.output.items():
track_id = cluster
"/Users/james/Cloud/Projects/ElectroMagnetic/reaper/Convolutions/anchors/media/07-glued.wav"
)
db = (Corpus("~/Cloud/Projects/DataBending/DataAudioUnique").duration(
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()]
from ftis.analyser.descriptor import FluidMFCC
from ftis.analyser.scaling import Standardise
from ftis.analyser.dr import UMAP
from ftis.analyser.clustering import HDBSCAN
from ftis.analyser.stats import Stats
from ftis.process import FTISProcess as Chain
from ftis.corpus import Corpus
db_corpus = (Corpus("~/Cloud/Projects/DataBending/DataAudioUnique").duration(
min_duration=0.03))
em_corpus = (Corpus("../outputs/em_detailed_segmentation/1_ExplodeAudio"))
analysis = Chain(source=db_corpus + em_corpus,
folder="../outputs/multicorpus_exploring")
analysis.add(
FluidMFCC(discard=True, numcoeffs=20, fftsettings=[2048, -1, -1], cache=1),
Stats(numderivs=1, flatten=True, cache=1), Standardise(),
UMAP(components=2), HDBSCAN(minclustersize=5))
if __name__ == "__main__":
analysis.run()