151 lines
4.2 KiB
Python
151 lines
4.2 KiB
Python
import sys,os,json,types
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib._color_data as mcd
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from matplotlib.pyplot import figure
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from rms_analysis import calibrate_recording_analysis
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def plot_by_pitch( inDir, pitch=None ):
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anlD = calibrate_recording_analysis( inDir )
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jsonFn = os.path.join(inDir, "meas.json" )
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audioFn = os.path.join(inDir, "audio.wav" )
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with open(jsonFn,"r") as f:
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r = json.load(f)
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measD = r['measD']
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cfg = types.SimpleNamespace(**r['cfg'])
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axN = len(measD) if pitch is None else 1
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fig,axL = plt.subplots(axN,1)
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fig.set_size_inches(18.5, 10.5*axN)
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# for each pitch
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for axi,(midi_pitch,measL)in enumerate(measD.items()):
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midi_pitch = int(midi_pitch)
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if pitch is not None and pitch != midi_pitch:
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continue
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if pitch is not None:
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axi = 0
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axL = [ axL ]
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targetDbS = set()
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hmPulseDbL = []
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tdPulseDbL = []
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anPulseDbL = []
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# for each measurement on this pitch
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for mi,d in enumerate(measL):
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m = types.SimpleNamespace(**d)
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# form a list of pulse/db measurements associated with this pitch
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hmPulseDbL.append( (m.pulse_us,m.hm['db'],m.matchFl,m.hm['durMs'],m.skipMeasFl) )
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tdPulseDbL.append( (m.pulse_us,m.td['db'],m.matchFl,m.td['durMs'],m.skipMeasFl) )
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ar = next(ad for ad in anlD[midi_pitch] if ad['meas_idx']==mi )
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anPulseDbL.append( (m.pulse_us,ar['db'],m.matchFl,m.hm['durMs'],m.skipMeasFl))
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# get the unique set of targets
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targetDbS.add(m.targetDb)
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# sort measurements on pulse length
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hmPulseDbL = sorted(hmPulseDbL,key=lambda x: x[0])
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tdPulseDbL = sorted(tdPulseDbL,key=lambda x: x[0])
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anPulseDbL = sorted(anPulseDbL,key=lambda x: x[0])
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# plot the re-analysis
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pulseL,dbL,matchFlL,_,_ = zip(*anPulseDbL)
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axL[axi].plot( pulseL, dbL, label="post", marker='.' )
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# plot harmonic measurements
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pulseL,dbL,matchFlL,durMsL,skipFlL = zip(*hmPulseDbL)
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axL[axi].plot( pulseL, dbL, label="harm", marker='.' )
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# plot time-domain based measuremented
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pulseL,dbL,matchFlL,_,_ = zip(*tdPulseDbL)
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axL[axi].plot( pulseL, dbL, label="td", marker='.' )
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# plot target boundaries
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for targetDb in targetDbS:
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lwr = targetDb * ((100.0 - cfg.tolDbPct)/100.0)
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upr = targetDb * ((100.0 + cfg.tolDbPct)/100.0 )
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axL[axi].axhline(targetDb)
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axL[axi].axhline(lwr,color='lightgray')
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axL[axi].axhline(upr,color='gray')
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# plot match and 'too-short' markers
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for i,matchFl in enumerate(matchFlL):
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if durMsL[i] < cfg.minMeasDurMs:
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axL[axi].plot( pulseL[i], dbL[i], marker='x', color='black', linestyle='None')
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if skipFlL[i]:
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axL[axi].plot( pulseL[i], dbL[i], marker='+', color='blue', linestyle='None')
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if matchFl:
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axL[axi].plot( pulseL[i], dbL[i], marker='.', color='red', linestyle='None')
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axL[axi].set_title("pitch:%i " % (midi_pitch))
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plt.legend()
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plt.show()
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def plot_all_notes( inDir ):
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jsonFn = os.path.join(inDir, "meas.json" )
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audioFn = os.path.join(inDir, "audio.wav" )
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with open(jsonFn,"r") as f:
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r = json.load(f)
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measD = r['measD']
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axN = 0
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for midi_pitch,measL in measD.items():
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axN += len(measL)
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print(axN)
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fig,axL = plt.subplots(axN,1)
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fig.set_size_inches(18.5, 10.5*axN)
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i = 0
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for midi_pitch,measL in measD.items():
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for d in measL:
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axL[i].plot(d['td']['rmsDbV'])
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axL[i].plot(d['hm']['rmsDbV'])
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axL[i].axvline(d['td']['pk_idx'],color='red')
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axL[i].axvline(d['hm']['pk_idx'],color='green')
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i += 1
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plt.show()
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if __name__ == "__main__":
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pitch = None
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inDir = sys.argv[1]
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if len(sys.argv) > 2:
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pitch = int(sys.argv[2])
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#plot_all_notes( inDir )
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plot_by_pitch(inDir,pitch)
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#calibrate_recording_analysis( inDir )
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