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