import os, sys,json
import matplotlib.pyplot as plt
import numpy as np
from common import parse_yaml_cfg
import rms_analysis
import elbow

def fit_to_reference( pkL, refTakeId ):

    us_outL  = []
    db_outL  = []
    dur_outL = []
    tid_outL = []

    dbL,usL,durMsL,takeIdL = tuple(zip(*pkL))
    
    us_refL,db_refL,dur_refL = zip(*[(usL[i],dbL[i],durMsL[i]) for i in range(len(usL)) if takeIdL[i]==refTakeId])

    
    for takeId in set(takeIdL):
        us0L,db0L,dur0L = zip(*[(usL[i],dbL[i],durMsL[i]) for i in range(len(usL)) if takeIdL[i]==takeId  ])
            

        if takeId == refTakeId:
            db_outL += db0L
        else:
            db1V = elbow.fit_points_to_reference(us0L,db0L,us_refL,db_refL)
            db_outL += db1V.tolist()

        us_outL += us0L
        dur_outL+= dur0L
        tid_outL+= [takeId] * len(us0L)
        
    return zip(db_outL,us_outL,dur_outL,tid_outL)


def get_merged_pulse_db_measurements( inDir, midi_pitch, analysisArgsD ):
    
    inDir = os.path.join(inDir,"%i" % (midi_pitch))

    takeDirL =  os.listdir(inDir)

    pkL = []

    usRefL = None
    dbRefL = None

    # for each take in this directory
    for take_number in range(len(takeDirL)):

        # analyze this takes audio and locate the note peaks
        r = rms_analysis.rms_analysis_main( os.path.join(inDir,str(take_number)), midi_pitch, **analysisArgsD )

        # store the peaks in pkL[ (db,us) ]
        for db,us,stats in zip(r.pkDbL,r.pkUsL,r.statsL):
            pkL.append( (db,us,stats.durMs,take_number) )

    pkL = fit_to_reference( pkL, 0 )
    
    # sort the peaks on increasing attack pulse microseconds
    pkL = sorted( pkL, key= lambda x: x[1] )

    # merge sample points that separated by less than 'minSampleDistUs' milliseconds
    #pkL = merge_close_sample_points( pkL, analysisArgsD['minSampleDistUs'] )
    
    # split pkL 
    pkDbL,pkUsL,durMsL,takeIdL = tuple(zip(*pkL))

    return pkUsL,pkDbL,durMsL,takeIdL,r.holdDutyPctL

    

    

    


def select_resample_reference_indexes( noiseIdxL ):

    resampleIdxS = set()

    # for each noisy sample index store that index and the index
    # before and after it
    for i in noiseIdxL:
        resampleIdxS.add( i )
        if i+1 < len(noiseIdxL):
            resampleIdxS.add( i+1 )
        if i-1 >= 0:
            resampleIdxS.add( i-1 )

    resampleIdxL = list(resampleIdxS)

    # if a single sample point is left out of a region of
    # contiguous sample points then include this as a resample point also
    for i in resampleIdxL:
        if i + 1 not in resampleIdxL and i + 2 in resampleIdxL:  # BUG BUG BUG: Hardcoded constant
            if i+1 < len(noiseIdxL):
                resampleIdxL.append(i+1)

    return resampleIdxL

def locate_resample_regions( usL, dbL, resampleIdxL ):

    # locate regions of points to resample
    regionL = []  # (bi,ei)
    inRegionFl = False
    bi = None
    for i in range(len(usL)):
        if inRegionFl:            
            if i not in resampleIdxL:
                regionL.append((bi,i-1))
                inRegionFl = False
                bi         = None
        else:
            if i in resampleIdxL:
                inRegionFl = True
                bi         = i
                
    if bi is not None:
        regionL.append((bi,len(usL)-1))

    # select points around and within the resample regions
    # to resample
    reUsL = []
    reDbL = []
    for bi,ei in regionL:

        for i in range(bi,ei+2):
            if i == 0:
                us = usL[i]
                db = dbL[i]
            elif i >= len(usL):
                us = usL[i-1]
                db = dbL[i-1]
            else:
                us = usL[i-1] + (usL[i]-usL[i-1])/2 
                db = dbL[i-1] + (dbL[i]-dbL[i-1])/2
                
            reUsL.append(us)
            reDbL.append(db)


    return reUsL,reDbL

def get_dur_skip_indexes( durMsL, dbL, takeIdL, scoreL, minDurMs, minDb, noiseLimitPct ):

    firstAudibleIdx = None
    firstNonSkipIdx = None

    # get the indexes of samples which do not meet the duration, db level, or noise criteria
    skipIdxL = [ i for i,(ms,db,score) in enumerate(zip(durMsL,dbL,scoreL)) if ms < minDurMs or db < minDb or score > noiseLimitPct ]

    # if a single sample point is left out of a region of
    # contiguous skipped points then skip this point also
    for i in range(len(durMsL)):
        if i not in skipIdxL and i-1 in skipIdxL and i+1 in skipIdxL:
            skipIdxL.append(i)

    # find the first set of 3 contiguous samples that
    # are greater than minDurMs - all samples prior
    # to these will be skipped
    xL = []
    for i in range(len(durMsL)):
        if i in skipIdxL:
            xL = []
        else:
            xL.append(i)

        if len(xL) == 3:  # BUG BUG BUG: Hardcoded constant
            firstAudibleIdx = xL[0]
            break
        

    # decrease by one decibel to locate the first non-skip

    # TODO: what if no note exists that is one decibel less
    #       The recordings of very quiet notes do not give reliabel decibel measures
    #       so this may not be the best backup criteria
    
    if firstAudibleIdx is not None:
        i = firstAudibleIdx-1
        while abs(dbL[i] - dbL[firstAudibleIdx]) < 1.0:  # BUG BUG BUG: Hardcoded constant
            i -= 1

        firstNonSkipIdx = i
        
    return skipIdxL, firstAudibleIdx, firstNonSkipIdx

def get_resample_points( usL, dbL, durMsL, takeIdL, minDurMs, minDb, noiseLimitPct ):

    scoreV       = np.abs( rms_analysis.samples_to_linear_residual( usL, dbL) * 100.0 / dbL )
    
    skipIdxL, firstAudibleIdx, firstNonSkipIdx = get_dur_skip_indexes( durMsL, dbL, takeIdL, scoreV.tolist(), minDurMs, minDb, noiseLimitPct )
    
    skipL         = [ (usL[i],dbL[i]) for i in skipIdxL ]
    noiseIdxL    = [ i for i in range(scoreV.shape[0]) if scoreV[i] > noiseLimitPct ]
    noiseL       = [ (usL[i],dbL[i]) for i in noiseIdxL ]
    resampleIdxL = select_resample_reference_indexes( noiseIdxL )
    
    if firstNonSkipIdx is not None:
        resampleIdxL = [ i for i in resampleIdxL if i >= firstNonSkipIdx ]
        
    resampleL    = [ (usL[i],dbL[i]) for i in resampleIdxL   ]
    reUsL,reDbL  = locate_resample_regions( usL, dbL, resampleIdxL )

    return reUsL, reDbL, noiseL, resampleL, skipL, firstAudibleIdx, firstNonSkipIdx

def get_resample_points_wrap( inDir, midi_pitch, analysisArgsD ):

    usL, dbL, durMsL,_,_ = get_merged_pulse_db_measurements( inDir, midi_pitch, analysisArgsD['rmsAnalysisArgs'] )

    reUsL,_,_,_,_,_,_ = get_resample_points( usL, dbL, durMsL, analysisArgsD['resampleMinDurMs'], analysisArgsD['resampleMinDb'], analysisArgsD['resampleNoiseLimitPct'] )
    
    return reUsL



def plot_us_db_curves( ax, inDir, keyMapD, midi_pitch, analysisArgsD, plotResamplePointsFl=False, plotTakesFl=True, usMax=None ):

    usL, dbL, durMsL, takeIdL, holdDutyPctL = get_merged_pulse_db_measurements( inDir, midi_pitch, analysisArgsD['rmsAnalysisArgs'] )
    reUsL, reDbL, noiseL, resampleL, skipL, firstAudibleIdx, firstNonSkipIdx  = get_resample_points( usL, dbL, durMsL, takeIdL, analysisArgsD['resampleMinDurMs'], analysisArgsD['resampleMinDb'], analysisArgsD['resampleNoiseLimitPct'] )

    # plot first audible and non-skip position
    if False:
    
        if firstNonSkipIdx is not None:
            ax.plot( usL[firstNonSkipIdx], dbL[firstNonSkipIdx], markersize=15, marker='+', linestyle='None', color='red')

        if firstAudibleIdx is not None:
            ax.plot( usL[firstAudibleIdx], dbL[firstAudibleIdx], markersize=15, marker='*', linestyle='None', color='red')

        # plot the resample points
        if plotResamplePointsFl:
            ax.plot( reUsL, reDbL, markersize=13, marker='x', linestyle='None', color='green')

        # plot the noisy sample positions
        if noiseL:
            nUsL,nDbL = zip(*noiseL)
            ax.plot( nUsL, nDbL, marker='o', markersize=9, linestyle='None', color='black')

        # plot the noisy sample positions and the neighbors included in the noisy region
        if resampleL:
            nUsL,nDbL    = zip(*resampleL)
            ax.plot( nUsL, nDbL, marker='+', markersize=8, linestyle='None', color='red')


        
    # plot actual sample points

    elbow_us  = None
    elbow_db  = None
    elbow_len = None

    usL,dbL,takeIdL = zip(*[(us,dbL[i],takeIdL[i]) for i,us in enumerate(usL) if usMax is None or us <= usMax])
    
    if plotTakesFl:
        for takeId in list(set(takeIdL)):

            # get the us,db points included in this take
            xL,yL = zip(*[(usL[i],dbL[i]) for i in range(len(usL)) if takeIdL[i]==takeId  ])
            
            ax.plot(xL,yL, marker='.',label=takeId)
            
            for i,(x,y) in enumerate(zip(xL,yL)):
                ax.text(x,y,str(i))


            #if elbow_len is None or len(xL) > elbow_len:
            if takeId+1 == len(set(takeIdL)):
                elbow_us,elbow_db = elbow.find_elbow(xL,yL)
                elbow_len = len(xL)
                
            
            
    else:
        ax.plot(usL, dbL, marker='.')

    ax.plot([elbow_us],[elbow_db],marker='*',markersize=12,color='red',linestyle='None')
        
    # plot the skip points in yellow
    if False:
        if skipL:
            nUsL,nDbL    = zip(*skipL)
            ax.plot( nUsL, nDbL, marker='.', linestyle='None', color='yellow')

    # plot the locations where the hold duty cycle changes with vertical black lines
    for us_duty in holdDutyPctL:
        us,duty = tuple(us_duty)
        if us > 0:
            ax.axvline(us,color='black')

    # plot the 'minDb' reference line
    ax.axhline(analysisArgsD['resampleMinDb'] ,color='black')

    if os.path.isfile("minInterpDb.json"):
        with open("minInterpDb.json","r") as f:
            r = json.load(f)
            if midi_pitch  in r['pitchL']:
                ax.axhline( r['minDbL'][ r['pitchL'].index(midi_pitch) ], color='blue' )
                ax.axhline( r['maxDbL'][ r['pitchL'].index(midi_pitch) ], color='blue' )
    
    ax.set_ylabel( "%i %s %s" % (midi_pitch, keyMapD[midi_pitch]['type'],keyMapD[midi_pitch]['class']))
    
def plot_us_db_curves_main( inDir, cfg, pitchL, plotTakesFl=True, usMax=None ):

    analysisArgsD = cfg.analysisArgs
    keyMapD = { d['midi']:d for d in cfg.key_mapL }
    axN = len(pitchL)
    fig,axL = plt.subplots(axN,1,sharex=True)
    if axN == 1:
        axL = [axL]
    fig.set_size_inches(18.5, 10.5*axN)

    for ax,midi_pitch in zip(axL,pitchL):
        plot_us_db_curves( ax,inDir, keyMapD, midi_pitch, analysisArgsD, plotTakesFl=plotTakesFl, usMax=usMax )

    if plotTakesFl:
        plt.legend()
        
    plt.show()

def plot_all_noise_curves( inDir, cfg, pitchL=None ):

    pitchFolderL = os.listdir(inDir)
    
    if pitchL is None:
        pitchL = [ int( int(pitchFolder) ) for pitchFolder in pitchFolderL ]

    fig,ax = plt.subplots()

    for midi_pitch in pitchL:

        print(midi_pitch)
        
        usL, dbL, durMsL, takeIdL, holdDutyPctL = get_merged_pulse_db_measurements( inDir, midi_pitch, cfg.analysisArgs['rmsAnalysisArgs'] )

        scoreV       = np.abs( rms_analysis.samples_to_linear_residual( usL, dbL) * 100.0 / dbL )
        
        minDurMs      = cfg.analysisArgs['resampleMinDurMs']
        minDb         = cfg.analysisArgs['resampleMinDb'],
        noiseLimitPct = cfg.analysisArgs['resampleNoiseLimitPct']

        skipIdxL, firstAudibleIdx, firstNonSkipIdx = get_dur_skip_indexes( durMsL, dbL, scoreV.tolist(), takeIdL, minDurMs, minDb, noiseLimitPct )
    

        if False:
            ax.plot( usL[firstAudibleIdx], scoreV[firstAudibleIdx], markersize=10, marker='*', linestyle='None', color='red')
            ax.plot( usL, scoreV, label="%i"%(midi_pitch) )
            ax.set_xlabel('us')

        else:
            xL = [ (score,db,i) for i,(score,db) in  enumerate(zip(scoreV,dbL)) ]
            xL = sorted(xL, key=lambda x: x[1] )

            scoreV,dbL,idxL = zip(*xL)
            ax.plot( dbL[idxL[firstAudibleIdx]], scoreV[idxL[firstAudibleIdx]], markersize=10, marker='*', linestyle='None', color='red')
            ax.plot( dbL, scoreV, label="%i"%(midi_pitch) )
            ax.set_xlabel('db')
        
        ax.set_ylabel("noise db %")
        
    plt.legend()
    plt.show()

def plot_min_max_2_db( inDir, cfg, pitchL=None, takeId=2 ):

    pitchFolderL = os.listdir(inDir)
    
    if pitchL is None:
        pitchL = [ int( int(pitchFolder) ) for pitchFolder in pitchFolderL ]


    okL   = []
    outPitchL = []
    minDbL = []
    maxDbL = []
    for midi_pitch in pitchL:

        print(midi_pitch)

        usL, dbL, durMsL, takeIdL, holdDutyPctL = get_merged_pulse_db_measurements( inDir, midi_pitch, cfg.analysisArgs['rmsAnalysisArgs'] )

        okL.append(False)

        takeId = len(set(takeIdL))-1

        db_maxL = sorted(dbL)
        maxDbL.append( np.mean(db_maxL[-5:]) )
        

        usL,dbL = zip(*[(usL[i],dbL[i]) for i in range(len(usL)) if takeIdL[i]==takeId  ])
        
        if len(set(takeIdL)) == 3:            
            okL[-1] = True
            
        elbow_us,elbow_db = elbow.find_elbow(usL,dbL)
        minDbL.append(elbow_db)
        outPitchL.append(midi_pitch)



    p_dL = sorted( zip(outPitchL,minDbL,maxDbL,okL), key=lambda x: x[0] )
    outPitchL,minDbL,maxDbL,okL = zip(*p_dL)
        
    fig,ax = plt.subplots()
    ax.plot(outPitchL,minDbL)
    ax.plot(outPitchL,maxDbL)
    
    keyMapD = { d['midi']:d for d in cfg.key_mapL }
    for pitch,min_db,max_db,okFl in zip(outPitchL,minDbL,maxDbL,okL):
        c = 'black' if okFl else 'red'
        ax.text( pitch, min_db, "%i %s %s" % (pitch, keyMapD[pitch]['type'],keyMapD[pitch]['class']), color=c)
        ax.text( pitch, max_db, "%i %s %s" % (pitch, keyMapD[pitch]['type'],keyMapD[pitch]['class']), color=c)


    plt.show()

def plot_min_db_manual( inDir, cfg ):
    
    pitchL     = list(cfg.manualMinD.keys())
    
    outPitchL = []
    maxDbL    = []
    minDbL    = []
    okL       = []
    anchorMinDbL = []
    anchorMaxDbL = []

    for midi_pitch in pitchL:

        manual_take_id    = cfg.manualMinD[midi_pitch][0]
        manual_sample_idx = cfg.manualMinD[midi_pitch][1]
        
        usL, dbL, durMsL, takeIdL, holdDutyPctL = get_merged_pulse_db_measurements( inDir, midi_pitch, cfg.analysisArgs['rmsAnalysisArgs'] )

        okL.append(False)

        takeId = len(set(takeIdL))-1

        # maxDb is computed on all takes (not just the specified take)
        db_maxL = sorted(dbL)
        max_db = np.mean(db_maxL[-4:])
        maxDbL.append( max_db )

        # get the us,db values for the specified take
        usL,dbL = zip(*[(usL[i],dbL[i]) for i in range(len(usL)) if takeIdL[i]==manual_take_id  ])

        # most pitches have 3 sample takes that do not
        if len(set(takeIdL)) == 3 and manual_take_id == takeId:
            okL[-1] = True

        # min db from the sample index manually specified in cfg
        manualMinDb = dbL[ manual_sample_idx ]
        
        minDbL.append( manualMinDb )
        outPitchL.append(midi_pitch)

        
        if midi_pitch in cfg.manualAnchorPitchMinDbL:
            anchorMinDbL.append( manualMinDb )

        if midi_pitch in cfg.manualAnchorPitchMaxDbL:
            anchorMaxDbL.append( max_db )
            
        

    # Form the complete set of min/max db levels for each pitch by interpolating the
    # db values between the manually selected anchor points.
    interpMinDbL = np.interp( pitchL, cfg.manualAnchorPitchMinDbL, anchorMinDbL )
    interpMaxDbL = np.interp( pitchL, cfg.manualAnchorPitchMaxDbL, anchorMaxDbL )
        
    fig,ax = plt.subplots()

    
    ax.plot(outPitchL,minDbL) # plot the manually selected minDb values
    ax.plot(outPitchL,maxDbL) # plot the  max db values
    
    # plot the interpolated minDb/maxDb values
    ax.plot(pitchL,interpMinDbL)
    ax.plot(pitchL,interpMaxDbL)

    
    keyMapD = { d['midi']:d for d in cfg.key_mapL }
    for pitch,min_db,max_db,okFl in zip(outPitchL,minDbL,maxDbL,okL):
        c = 'black' if okFl else 'red'
        ax.text( pitch, min_db, "%i %s %s" % (pitch, keyMapD[pitch]['type'],keyMapD[pitch]['class']), color=c)
        ax.text( pitch, max_db, "%i %s %s" % (pitch, keyMapD[pitch]['type'],keyMapD[pitch]['class']), color=c)

    with open("minInterpDb.json",'w') as f:
        json.dump( { "pitchL":pitchL, "minDbL":list(interpMinDbL), "maxDbL":list(interpMaxDbL) }, f )

            
            
            
              


    plt.show()
    
def plot_min_max_db( inDir, cfg, pitchL=None ):

    pitchFolderL = os.listdir(inDir)
    
    if pitchL is None:
        pitchL = [ int( int(pitchFolder) ) for pitchFolder in pitchFolderL ]


    maxDbL = []
    minDbL = []
    for midi_pitch in pitchL:

        print(midi_pitch)
        
        usL, dbL, durMsL, takeIdL, holdDutyPctL = get_merged_pulse_db_measurements( inDir, midi_pitch, cfg.analysisArgs['rmsAnalysisArgs'] )

        scoreV       = np.abs( rms_analysis.samples_to_linear_residual( usL, dbL) * 100.0 / dbL )

        minDurMs      = cfg.analysisArgs['resampleMinDurMs']
        minDb         = cfg.analysisArgs['resampleMinDb'],
        noiseLimitPct = cfg.analysisArgs['resampleNoiseLimitPct']

        skipIdxL, firstAudibleIdx, firstNonSkipIdx = get_dur_skip_indexes( durMsL, dbL, takeIdL, scoreV.tolist(), minDurMs, minDb, noiseLimitPct )        

        minDbL.append( dbL[firstAudibleIdx] )
        
        dbL = sorted(dbL)

        x = np.mean(dbL[-3:])
        x = np.max(dbL)
        maxDbL.append( x )

        
    fig,ax = plt.subplots()

    fig.set_size_inches(18.5, 10.5)

    p_dL = sorted( zip(pitchL,maxDbL), key=lambda x: x[0] )
    pitchL,maxDbL = zip(*p_dL)

    ax.plot(pitchL,maxDbL)
    ax.plot(pitchL,minDbL)
    
    for pitch,db in zip(pitchL,maxDbL):

        keyMapD = { d['midi']:d for d in cfg.key_mapL }

        ax.text( pitch, db, "%i %s %s" % (pitch, keyMapD[pitch]['type'],keyMapD[pitch]['class']))


    plt.show()
    
def estimate_us_to_db_map( inDir, cfg, minMapDb=16.0, maxMapDb=26.0, incrMapDb=0.5, pitchL=None ):
    
    pitchFolderL = os.listdir(inDir)
    
    if pitchL is None:
        pitchL = [ int( int(pitchFolder) ) for pitchFolder in pitchFolderL ]

    mapD = {}  # pitch:{ loDb: { hiDb, us_avg, us_cls, us_std, us_min, us_max, db_avg, db_std, cnt }}
               #                  where: cnt=count of valid sample points in this db range
               #                         us_cls=us of closest point to center of db range
               
    dbS = set() # { (loDb,hiDb) }  track the set of db ranges
    
    for pitch in pitchL:

        print(pitch)

        # get the sample measurements for pitch
        usL, dbL, durMsL, takeIdL, holdDutyPctL = get_merged_pulse_db_measurements( inDir, pitch, cfg.analysisArgs['rmsAnalysisArgs'] )

        # calc the fit to local straight line curve fit at each point
        scoreV = np.abs( rms_analysis.samples_to_linear_residual( usL, dbL) * 100.0 / dbL )
        
        minDurMs      = cfg.analysisArgs['resampleMinDurMs']
        minDb         = cfg.analysisArgs['resampleMinDb'],
        noiseLimitPct = cfg.analysisArgs['resampleNoiseLimitPct']

        # get the set of samples that are not valid (too short, too quiet, too noisy)
        skipIdxL, firstAudibleIdx, firstNonSkipIdx = get_dur_skip_indexes( durMsL, dbL, takeIdL, scoreV.tolist(), minDurMs, minDb, noiseLimitPct )
    
        mapD[ pitch ] = {}
        
        # get the count of  db ranges
        N = int(round((maxMapDb - minMapDb) / incrMapDb)) + 1

        # for each db range
        for i in range(N):
            
            loDb = minMapDb + (i*incrMapDb)
            hiDb = loDb + incrMapDb

            dbS.add((loDb,hiDb))
            
            # get the valid (pulse,db) pairs for this range
            u_dL = [(us,db) for i,(us,db) in enumerate(zip(usL,dbL)) if i not in skipIdxL and loDb<=db and db<hiDb ]

            us_avg = 0
            us_cls = 0
            us_std = 0
            us_min = 0
            us_max = 0
            db_avg = 0
            db_std = 0
            
            if len(u_dL) == 0:
                print("No valid samples for pitch:",pitch," db range:",loDb,hiDb)
            else:
                us0L,db0L = zip(*u_dL)
                
                if len(us0L) == 1:
                    us_avg = us0L[0]
                    us_cls = us_avg
                    us_min = us_avg
                    us_max = us_avg
                    db_avg = db0L[0]

                elif len(us0L) > 1:
                    us_avg = np.mean(us0L)
                    us_cls = us0L[ np.argmin(np.abs(np.array(db0L)-(loDb - (hiDb-loDb)/2.0 ))) ]
                    us_min = np.min(us0L)
                    us_max = np.max(us0L)
                    us_std = np.std(us0L)
                    db_avg = np.mean(db0L)
                    db_std = np.std(db0L)

                us_avg = int(round(us_avg))

            
            mapD[pitch][loDb] = { 'hiDb':hiDb, 'us_avg':us_avg, 'us_cls':us_cls, 'us_std':us_std,'us_min':us_min,'us_max':us_max, 'db_avg':db_avg, 'db_std':db_std, 'cnt':len(u_dL) }
            
    return mapD, list(dbS)

def plot_us_to_db_map( inDir, cfg, minMapDb=16.0, maxMapDb=26.0, incrMapDb=1.0, pitchL=None ):

    fig,ax = plt.subplots()
    
    mapD, dbRefL = estimate_us_to_db_map( inDir, cfg, minMapDb, maxMapDb, incrMapDb, pitchL )

    # for each pitch
    for pitch, dbD in mapD.items():

        u_dL = [ (d['us_avg'],d['us_cls'],d['db_avg'],d['us_std'],d['us_min'],d['us_max'],d['db_std']) for loDb, d in  dbD.items() if d['us_avg'] != 0 ]

        # get the us/db lists for this pitch
        usL,uscL,dbL,ussL,usnL,usxL,dbsL = zip(*u_dL)

        # plot central curve and std dev's
        p = ax.plot(usL,dbL, marker='.', label=str(pitch))
        ax.plot(uscL,dbL, marker='x', label=str(pitch), color=p[0].get_color(), linestyle='None')
        ax.plot(usL,np.array(dbL)+dbsL, color=p[0].get_color(), alpha=0.3)
        ax.plot(usL,np.array(dbL)-dbsL, color=p[0].get_color(), alpha=0.3)

        # plot us error bars
        for db,us,uss,us_min,us_max in zip(dbL,usL,ussL,usnL,usxL):
            ax.plot([us_min,us_max],[db,db], color=p[0].get_color(), alpha=0.3 )
            ax.plot([us-uss,us+uss],[db,db], color=p[0].get_color(), alpha=0.3, marker='.', linestyle='None' )

                    
    plt.legend()
    plt.show()

def report_take_ids( inDir ):

    pitchDirL = os.listdir(inDir)

    for pitch in pitchDirL:

        pitchDir = os.path.join(inDir,pitch)
        
        takeDirL = os.listdir(pitchDir)

        if len(takeDirL) == 0:
            print(pitch," directory empty")
        else:
            with open( os.path.join(pitchDir,'0','seq.json'), "rb") as f:
                r = json.load(f)

            if len(r['eventTimeL']) != 81:
                print(pitch," ",len(r['eventTimeL']))

            if len(takeDirL) != 3:
                print("***",pitch,len(takeDirL))

def cache_us_db( inDir, cfg, outFn ):

    pitch_usDbD = {}
    pitchDirL = os.listdir(inDir)

    for pitch in pitchDirL:

        pitch = int(pitch)

        print(pitch)
        
        usL, dbL, durMsL, takeIdL, holdDutyPctL = get_merged_pulse_db_measurements( inDir, pitch, cfg.analysisArgs['rmsAnalysisArgs'] )

        pitch_usDbD[pitch] = { 'usL':usL, 'dbL':dbL, 'durMsL':durMsL, 'takeIdL':takeIdL, 'holdDutyPctL': holdDutyPctL }


    with open(outFn,"w") as f:
        json.dump(pitch_usDbD,f)

        
            
def gen_vel_map( inDir, cfg, minMaxDbFn, dynLevelN, cacheFn ):

    velMapD = {} # { pitch:[ us ] }
    
    pitchDirL = os.listdir(inDir)

    with open(cacheFn,"r") as f:
        pitchUsDbD = json.load(f)
        

    with open("minInterpDb.json","r") as f:
        r = json.load(f)
        minMaxDbD = { pitch:(minDb,maxDb) for pitch,minDb,maxDb in zip(r['pitchL'],r['minDbL'],r['maxDbL']) }
    

    pitchL = sorted( [ int(pitch) for pitch in pitchUsDbD.keys()] )
    
    for pitch in pitchL:
        d = pitchUsDbD[str(pitch)]
        
        usL = d['usL']
        dbL = np.array(d['dbL'])

        velMapD[pitch] = []
        
        for i in range(dynLevelN+1):

            db = minMaxDbD[pitch][0] + (i * (minMaxDbD[pitch][1] - minMaxDbD[pitch][0])/ dynLevelN)

            usIdx = np.argmin( np.abs(dbL - db) )
            
            velMapD[pitch].append( (usL[ usIdx ],db) )
        

            
    with open("velMapD.json","w") as f:
        json.dump(velMapD,f)
                
    mtx = np.zeros((len(velMapD),dynLevelN+1))
    print(mtx.shape)
    
    for i,(pitch,usDbL) in enumerate(velMapD.items()):
        for j in range(len(usDbL)):
            mtx[i,j] = usDbL[j][1]
            
    fig,ax = plt.subplots()
    ax.plot(pitchL,mtx)
    plt.show()
        
        

        
    
if __name__ == "__main__":

    inDir = sys.argv[1]
    cfgFn = sys.argv[2]
    mode  = sys.argv[3]
    if len(sys.argv) <= 4:
        pitchL = None
    else:
        pitchL = [ int(sys.argv[i]) for i in range(4,len(sys.argv)) ]

    cfg = parse_yaml_cfg( cfgFn )

    if mode == 'us_db':
        plot_us_db_curves_main( inDir, cfg, pitchL, plotTakesFl=True,usMax=None )
    elif mode == 'noise':
        plot_all_noise_curves( inDir, cfg, pitchL )
    elif mode == 'min_max':
        plot_min_max_db( inDir, cfg, pitchL )
    elif mode == 'min_max_2':
        plot_min_max_2_db( inDir, cfg, pitchL )
    elif mode == 'us_db_map':
        plot_us_to_db_map( inDir, cfg, pitchL=pitchL )
    elif mode == 'audacity':
        rms_analysis.write_audacity_label_files( inDir, cfg.analysisArgs['rmsAnalysisArgs'] )
    elif mode == 'rpt_take_ids':
        report_take_ids( inDir )
    elif mode == 'manual_db':
        plot_min_db_manual( inDir, cfg )
    elif mode == 'gen_vel_map':
        gen_vel_map( inDir, cfg, "minInterpDb.json", 9, "cache_us_db.json" )
    elif mode == 'cache_us_db':
        cache_us_db( inDir, cfg, "cache_us_db.json")
    else:
        print("Unknown mode:",mode)