def procesado(x):
""" Función principal para el procesado del audio a analizar para cada
ciclo de ejecución, 'x'. """
# Manejo de datos temporales.
ahora = tiempo() # instante de arranque para obtener la duración del ciclo
print('\nCalculando ...')
inicio = localtime(tiempo()-len(x)/fs) # tiempo de inicio del audio a analizar
fin = localtime() # tiempo final del audio a analizar
t_inic = np.array([inicio.tm_hour, inicio.tm_min, inicio.tm_sec])
dd = inicio.tm_mday
mn = inicio.tm_mon
yy = inicio.tm_year
# Procesamiento de la señal de audio.
xA = filt_A(x.reshape(-1)) # ponderación 'A'
xA_filt = filt_oct(xA) # filtrado por octavas
# Cálculo de niveles de presión con compensación.
niv_y = np.transpose(niveles(rms_t(xA_filt), vcal, ncal)) + ajuste
# Agregado de niveles globales.
glob_y = np.apply_along_axis(sum_db, 1, niv_y).reshape(niv_y.shape[0],1)
niv_y = np.hstack((niv_y, glob_y))
# Agregado de tiempos de inicio para la matriz de salida.
t = agregar_t(t_inic)
mat = np.hstack((t, niv_y))
# Escritura de la matriz de salida en el archivo de volcado '.npy'.
if not inicio.tm_mday == fin.tm_mday: # si hubo cambio de día, separo los resultados
separar(yy, mn, dd, fin, mat)
else:
escribir(yy, mn, dd, mat) # si no escribo directo la matriz
# Impresión en consola de tiempo de ejecución y nivel global del ciclo.
print('\nEjecución de ciclo completa. \nTiempo de ejecución : ' + str(np.around(tiempo() - ahora, 2)) + ' s\n')
print('Nivel global = ' + str(np.around(glob_y, 2)) +' dBA')
return
def tarea_generar_plan_ple(parametros_tarea, estadisticas_tarea):
print("INICIO generación plan PLE con id {}".format(parametros_tarea["id_plan_estudios"]))
estadisticas = EstadisticasDTO()
estadisticas.cargar_desde_JSON(estadisticas_tarea)
inicio = tiempo()
estadisticas.fecha_inicio_generacion = get_str_fecha_y_hora_actual()
parametros = Parametros()
parametros.actualizar_valores_desde_JSON(parametros_tarea)
generar_y_ejecutar_codigo_PULP(parametros)
estadisticas.fecha_fin_generacion = get_str_fecha_y_hora_actual()
estadisticas.segundos_total_generacion = float(tiempo() - inicio)
estadisticas.tiempo_total_generacion = convertir_tiempo(tiempo() - inicio)
print("FIN generación plan PLE con id {}".format(parametros_tarea["id_plan_estudios"]))
print("Se invoca al guardado para el plan PLE con id {}".format(parametros_tarea["id_plan_estudios"]))
from app.API_Rest.GeneradorPlanCarreras.broker_guardar_plan_generado import tarea_guadar_plan_de_estudios
tarea_guadar_plan_de_estudios.delay(parametros.generar_parametros_json(), estadisticas.get_JSON())
def archivo_existe(nombre):
if os.path.isfile(nombre):
print("Abriendo...")
tiempo(2)
else:
print("no existe el archivo con nombre {}".format(nombre))
# WHAT TO ANALYSE
# ===============
# comma separated list: {'KF100', KF1000', 'GSANG', 'ENDO100','DJTECJTOOLS60'}
collection = ['KF100', 'KF1000', 'GSANG', 'ENDO100','DJTECHTOOLS60']
# comma separated list: {'edm', 'non-edm'}
genre = ['edm']
# comma separated list: {'major', 'minor'}
modality = ['minor', 'major']
# Limit the analysis to n RANDOM songs. 0 analyses all the collection:
limit_analysis = 0
verbose = True
results_to_file = True
#create directory and unique time identifier
if results_to_file:
uniqueTime = str(int(tiempo()))
temp_folder = '/Users/angel/KeyDetection_'+uniqueTime
os.mkdir(temp_folder)
# retrieve filenames according to the desired settings...
allfiles = os.listdir(audio_folder)
if '.DS_Store' in allfiles:
allfiles.remove('.DS_Store')
for item in collection:
collection[collection.index(item)] = ' > ' + item + ' =='
for item in genre:
genre[genre.index(item)] = ' < ' + item + ' > '
for item in modality:
# WHAT TO ANALYSE
# ===============
# comma separated list: {'KF100', KF1000', 'GSANG', 'ENDO100','DJTECJTOOLS60'}
collection = ['KF100', 'KF1000', 'GSANG', 'ENDO100', 'DJTECHTOOLS60']
# comma separated list: {'edm', 'non-edm'}
genre = ['edm']
# comma separated list: {'major', 'minor'}
modality = ['minor', 'major']
# Limit the analysis to n RANDOM songs. 0 analyses all the collection:
limit_analysis = 0
verbose = True
results_to_file = True
#create directory and unique time identifier
if results_to_file:
uniqueTime = str(int(tiempo()))
temp_folder = '/Users/angel/KeyDetection_' + uniqueTime
os.mkdir(temp_folder)
# retrieve filenames according to the desired settings...
allfiles = os.listdir(audio_folder)
if '.DS_Store' in allfiles:
allfiles.remove('.DS_Store')
for item in collection:
collection[collection.index(item)] = ' > ' + item + ' =='
for item in genre:
genre[genre.index(item)] = ' < ' + item + ' > '
for item in modality:
def key_detector():
reloj()
# create directory to write the results with an unique time id:
if results_to_file or results_to_csv:
uniqueTime = str(int(tiempo()))
wd = os.getcwd()
temp_folder = wd + '/KeyDetection_'+uniqueTime
os.mkdir(temp_folder)
if results_to_csv:
import csv
csvFile = open(temp_folder + '/Estimation_&_PCP.csv', 'w')
lineWriter = csv.writer(csvFile, delimiter=',')
# retrieve files and filenames according to the desired settings:
if analysis_mode == 'title':
allfiles = os.listdir(audio_folder)
if '.DS_Store' in allfiles: allfiles.remove('.DS_Store')
for item in collection: collection[collection.index(item)] = ' > ' + item + '.'
for item in genre: genre[genre.index(item)] = ' < ' + item + ' > '
for item in modality:modality[modality.index(item)] = ' ' + item + ' < '
analysis_files = []
for item in allfiles:
if any(e1 for e1 in collection if e1 in item):
if any(e2 for e2 in genre if e2 in item):
if any(e3 for e3 in modality if e3 in item):
analysis_files.append(item)
song_instances = len(analysis_files)
print song_instances, 'songs matching the selected criteria:'
print collection, genre, modality
if limit_analysis == 0:
pass
elif limit_analysis < song_instances:
analysis_files = sample(analysis_files, limit_analysis)
print "taking", limit_analysis, "random samples...\n"
else:
analysis_files = os.listdir(audio_folder)
if '.DS_Store' in analysis_files:
analysis_files.remove('.DS_Store')
print len(analysis_files), '\nsongs in folder.\n'
groundtruth_files = os.listdir(groundtruth_folder)
if '.DS_Store' in groundtruth_files:
groundtruth_files.remove('.DS_Store')
# ANALYSIS
# ========
if verbose:
print "ANALYSING INDIVIDUAL SONGS..."
print "============================="
if confusion_matrix:
matrix = 24 * 24 * [0]
mirex_scores = []
for item in analysis_files:
# INSTANTIATE ESSENTIA ALGORITHMS
# ===============================
loader = estd.MonoLoader(filename=audio_folder+'/'+item,
sampleRate=sample_rate)
cut = estd.FrameCutter(frameSize=window_size,
hopSize=hop_size)
window = estd.Windowing(size=window_size,
type=window_type)
rfft = estd.Spectrum(size=window_size)
sw = estd.SpectralWhitening(maxFrequency=max_frequency,
sampleRate=sample_rate)
speaks = estd.SpectralPeaks(magnitudeThreshold=magnitude_threshold,
maxFrequency=max_frequency,
minFrequency=min_frequency,
maxPeaks=max_peaks,
sampleRate=sample_rate)
hpcp = estd.HPCP(bandPreset=band_preset,
harmonics=harmonics,
maxFrequency=max_frequency,
minFrequency=min_frequency,
nonLinear=non_linear,
normalized=normalize,
referenceFrequency=reference_frequency,
sampleRate=sample_rate,
size=hpcp_size,
splitFrequency=split_frequency,
weightType=weight_type,
windowSize=weight_window_size)
key = estd.Key(numHarmonics=num_harmonics,
pcpSize=hpcp_size,
profileType=profile_type,
slope=slope,
usePolyphony=use_polyphony,
useThreeChords=use_three_chords)
# ACTUAL ANALYSIS
# ===============
audio = loader()
duration = len(audio)
if skip_first_minute and duration > (sample_rate*60):
audio = audio[sample_rate*60:]
duration = len(audio)
if first_n_secs > 0:
if duration > (first_n_secs * sample_rate):
audio = audio[:first_n_secs * sample_rate]
duration = len(audio)
if avoid_edges > 0:
initial_sample = (avoid_edges * duration) / 100
final_sample = duration - initial_sample
audio = audio[initial_sample:final_sample]
duration = len(audio)
number_of_frames = duration / hop_size
chroma = []
for bang in range(number_of_frames):
spek = rfft(window(cut(audio)))
p1, p2 = speaks(spek) # p1 are frequencies; p2 magnitudes
if spectral_whitening:
p2 = sw(spek, p1, p2)
vector = hpcp(p1,p2)
sum_vector = np.sum(vector)
if sum_vector > 0:
if shift_spectrum == False or shift_scope == 'average':
chroma.append(vector)
elif shift_spectrum and shift_scope == 'frame':
vector = shift_vector(vector, hpcp_size)
chroma.append(vector)
else:
print "shift_scope must be set to 'frame' or 'average'"
chroma = np.mean(chroma, axis=0)
if shift_spectrum and shift_scope == 'average':
chroma = shift_vector(chroma, hpcp_size)
estimation = key(chroma.tolist())
result = estimation[0] + ' ' + estimation[1]
confidence = estimation[2]
if results_to_csv:
chroma = list(chroma)
# MIREX EVALUATION:
# ================
if analysis_mode == 'title':
ground_truth = item[item.find(' = ')+3:item.rfind(' < ')]
if verbose and confidence < confidence_threshold:
print item[:item.rfind(' = ')]
print 'G:', ground_truth, '|| P:',
if results_to_csv:
title = item[:item.rfind(' = ')]
lineWriter.writerow([title, ground_truth, chroma[0], chroma[1], chroma[2], chroma[3], chroma[4], chroma[5], chroma[6], chroma[7], chroma[8], chroma[9], chroma[10], chroma[11], chroma[12], chroma[13], chroma[14], chroma[15], chroma[16], chroma[17], chroma[18], chroma[19], chroma[20], chroma[21], chroma[22], chroma[23], chroma[24], chroma[25], chroma[26], chroma[27], chroma[28], chroma[29], chroma[30], chroma[31], chroma[32], chroma[33], chroma[34], chroma[35], result])
ground_truth = key_to_list(ground_truth)
estimation = key_to_list(result)
score = mirex_score(ground_truth, estimation)
mirex_scores.append(score)
else:
filename_to_match = item[:item.rfind('.')] + '.txt'
print filename_to_match
if filename_to_match in groundtruth_files:
groundtruth_file = open(groundtruth_folder+'/'+filename_to_match, 'r')
ground_truth = groundtruth_file.readline()
if "\t" in ground_truth:
ground_truth = re.sub("\t", " ", ground_truth)
if results_to_csv:
lineWriter.writerow([filename_to_match, chroma[0], chroma[1], chroma[2], chroma[3], chroma[4], chroma[5], chroma[6], chroma[7], chroma[8], chroma[9], chroma[10], chroma[11], chroma[12], chroma[13], chroma[14], chroma[15], chroma[16], chroma[17], chroma[18], chroma[19], chroma[20], chroma[21], chroma[22], chroma[23], chroma[24], chroma[25], chroma[26], chroma[27], chroma[28], chroma[29], chroma[30], chroma[31], chroma[32], chroma[33], chroma[34], chroma[35], result])
ground_truth = key_to_list(ground_truth)
estimation = key_to_list(result)
score = mirex_score(ground_truth, estimation)
mirex_scores.append(score)
else:
print "FILE NOT FOUND... Skipping it from evaluation.\n"
continue
# CONFUSION MATRIX:
# ================
if confusion_matrix:
xpos = (ground_truth[0] + (ground_truth[0] * 24)) + (-1*(ground_truth[1]-1) * 24 * 12)
ypos = ((estimation[0] - ground_truth[0]) + (-1 * (estimation[1]-1) * 12))
matrix[(xpos+ypos)] =+ matrix[(xpos+ypos)] + 1
if verbose and confidence < confidence_threshold:
print result, '(%.2f)' % confidence, '|| SCORE:', score, '\n'
# WRITE RESULTS TO FILE:
# =====================
if results_to_file:
with open(temp_folder + '/' + item[:-3]+'txt', 'w') as textfile:
textfile.write(result)
textfile.close()
if results_to_csv:
csvFile.close()
print len(mirex_scores), "files analysed in", reloj(), "secs.\n"
if confusion_matrix:
matrix = np.matrix(matrix)
matrix = matrix.reshape(24,24)
print matrix
if results_to_file:
np.savetxt(temp_folder + '/_confusion_matrix.csv', matrix, fmt='%i', delimiter=',', header='C,C#,D,Eb,E,F,F#,G,G#,A,Bb,B,Cm,C#m,Dm,Ebm,Em,Fm,F#m,Gm,G#m,Am,Bbm,Bm')
# MIREX RESULTS
# =============
evaluation_results = mirex_evaluation(mirex_scores)
# WRITE INFO TO FILE
# ==================
if results_to_file:
settings = "SETTINGS\n========\nAvoid edges ('%' of duration disregarded at both ends (0 = complete)) = "+str(avoid_edges)+"\nfirst N secs = "+str(first_n_secs)+"\nshift spectrum to fit tempered scale = "+str(shift_spectrum)+"\nspectral whitening = "+str(spectral_whitening)+"\nsample rate = "+str(sample_rate)+"\nwindow size = "+str(window_size)+"\nhop size = "+str(hop_size)+"\nmagnitude threshold = "+str(magnitude_threshold)+"\nminimum frequency = "+str(min_frequency)+"\nmaximum frequency = "+str(max_frequency)+"\nmaximum peaks = "+str(max_peaks)+"\nband preset = "+str(band_preset)+"\nsplit frequency = "+str(split_frequency)+"\nharmonics = "+str(harmonics)+"\nnon linear = "+str(non_linear)+"\nnormalize = "+str(normalize)+"\nreference frequency = "+str(reference_frequency)+"\nhpcp size = "+str(hpcp_size)+"\nweigth type = "+weight_type+"\nweight window size in semitones = "+str(weight_window_size)+"\nharmonics key = "+str(num_harmonics)+"\nslope = "+str(slope)+"\nprofile = "+profile_type+"\npolyphony = "+str(use_polyphony)+"\nuse three chords = "+str(use_three_chords)
results_for_file = "\n\nEVALUATION RESULTS\n==================\nCorrect: "+str(evaluation_results[0])+"\nFifth: "+str(evaluation_results[1])+"\nRelative: "+str(evaluation_results[2])+"\nParallel: "+str(evaluation_results[3])+"\nError: "+str(evaluation_results[4])+"\nWeighted: "+str(evaluation_results[5])
write_to_file = open(temp_folder + '/_SUMMARY.txt', 'w')
write_to_file.write(settings)
write_to_file.write(results_for_file)
if analysis_mode == 'title':
corpus = "\n\nANALYSIS CORPUS\n===============\n" + str(collection) + '\n' + str(genre) + '\n' + str(modality) + '\n\n' + str(len(mirex_scores)) + " files analysed.\n"
write_to_file.write(corpus)
write_to_file.close()
(subrayar, sColor, v,
dConc.get(sConc, {'des':'NO TENGO DESCRIPCION'})['des'] + '-' + sNuc,
FG.formateaNumero(dicc[v][0]), FG.formateaNumero(dicc[v][1], 2),
FG.formateaNumero(dicc[v][2], 2), sObs, CO.FIN)
return st
# FIN funcion mostrarConceptos
# Inicio principal
sConc = '563'
dConc = CC.creaDicConceptos()
# 1:mes(1-12); 2:dia(1-31); 3:hora(0-23); 4:min(0-59); 5:seg(0-60);
# 6:dia sem(0 a 6 (o lunes)); 7:dia a#o(1-366); 8:Verano(-1,0,1,-1)
sMes = str(tiempo()[1])
sAno = str(tiempo()[0])
if not bMovil:
if 1 < len(sys.argv):
mes = sys.argv[1]
if not mes.isdigit() or 1 > int(mes) or 12 < int(mes):
print("%sEl primer prametro deberia ser el mes y esta errado.%s" %
(CO.ROJO, CO.FIN))
sys.exit()
if 2 < len(sys.argv):
nombArchivo = sys.argv[2]
else:
nombArchivo = 'IPAS'
if 3 < len(sys.argv) and sys.argv[3].isdigit():
ano = sys.argv[3]
else:
def key_detector():
reloj()
# create directory to write the results with an unique time id:
if results_to_file or results_to_csv:
uniqueTime = str(int(tiempo()))
wd = os.getcwd()
temp_folder = wd + '/KeyDetection_' + uniqueTime
os.mkdir(temp_folder)
if results_to_csv:
import csv
csvFile = open(temp_folder + '/Estimation_&_PCP.csv', 'w')
lineWriter = csv.writer(csvFile, delimiter=',')
# retrieve files and filenames according to the desired settings:
if analysis_mode == 'title':
allfiles = os.listdir(audio_folder)
if '.DS_Store' in allfiles: allfiles.remove('.DS_Store')
for item in collection:
collection[collection.index(item)] = ' > ' + item + '.'
for item in genre:
genre[genre.index(item)] = ' < ' + item + ' > '
for item in modality:
modality[modality.index(item)] = ' ' + item + ' < '
analysis_files = []
for item in allfiles:
if any(e1 for e1 in collection if e1 in item):
if any(e2 for e2 in genre if e2 in item):
if any(e3 for e3 in modality if e3 in item):
analysis_files.append(item)
song_instances = len(analysis_files)
print song_instances, 'songs matching the selected criteria:'
print collection, genre, modality
if limit_analysis == 0:
pass
elif limit_analysis < song_instances:
analysis_files = sample(analysis_files, limit_analysis)
print "taking", limit_analysis, "random samples...\n"
else:
analysis_files = os.listdir(audio_folder)
if '.DS_Store' in analysis_files:
analysis_files.remove('.DS_Store')
print len(analysis_files), '\nsongs in folder.\n'
groundtruth_files = os.listdir(groundtruth_folder)
if '.DS_Store' in groundtruth_files:
groundtruth_files.remove('.DS_Store')
# ANALYSIS
# ========
if verbose:
print "ANALYSING INDIVIDUAL SONGS..."
print "============================="
if confusion_matrix:
matrix = 24 * 24 * [0]
mirex_scores = []
for item in analysis_files:
# INSTANTIATE ESSENTIA ALGORITHMS
# ===============================
loader = estd.MonoLoader(filename=audio_folder + '/' + item,
sampleRate=sample_rate)
cut = estd.FrameCutter(frameSize=window_size, hopSize=hop_size)
window = estd.Windowing(size=window_size, type=window_type)
rfft = estd.Spectrum(size=window_size)
sw = estd.SpectralWhitening(maxFrequency=max_frequency,
sampleRate=sample_rate)
speaks = estd.SpectralPeaks(magnitudeThreshold=magnitude_threshold,
maxFrequency=max_frequency,
minFrequency=min_frequency,
maxPeaks=max_peaks,
sampleRate=sample_rate)
hpcp = estd.HPCP(bandPreset=band_preset,
harmonics=harmonics,
maxFrequency=max_frequency,
minFrequency=min_frequency,
nonLinear=non_linear,
normalized=normalize,
referenceFrequency=reference_frequency,
sampleRate=sample_rate,
size=hpcp_size,
splitFrequency=split_frequency,
weightType=weight_type,
windowSize=weight_window_size)
key = estd.Key(numHarmonics=num_harmonics,
pcpSize=hpcp_size,
profileType=profile_type,
slope=slope,
usePolyphony=use_polyphony,
useThreeChords=use_three_chords)
# ACTUAL ANALYSIS
# ===============
audio = loader()
duration = len(audio)
if skip_first_minute and duration > (sample_rate * 60):
audio = audio[sample_rate * 60:]
duration = len(audio)
if first_n_secs > 0:
if duration > (first_n_secs * sample_rate):
audio = audio[:first_n_secs * sample_rate]
duration = len(audio)
if avoid_edges > 0:
initial_sample = (avoid_edges * duration) / 100
final_sample = duration - initial_sample
audio = audio[initial_sample:final_sample]
duration = len(audio)
number_of_frames = duration / hop_size
chroma = []
for bang in range(number_of_frames):
spek = rfft(window(cut(audio)))
p1, p2 = speaks(spek) # p1 are frequencies; p2 magnitudes
if spectral_whitening:
p2 = sw(spek, p1, p2)
vector = hpcp(p1, p2)
sum_vector = np.sum(vector)
if sum_vector > 0:
if shift_spectrum == False or shift_scope == 'average':
chroma.append(vector)
elif shift_spectrum and shift_scope == 'frame':
vector = shift_vector(vector, hpcp_size)
chroma.append(vector)
else:
print "shift_scope must be set to 'frame' or 'average'"
chroma = np.mean(chroma, axis=0)
if shift_spectrum and shift_scope == 'average':
chroma = shift_vector(chroma, hpcp_size)
estimation = key(chroma.tolist())
result = estimation[0] + ' ' + estimation[1]
confidence = estimation[2]
if results_to_csv:
chroma = list(chroma)
# MIREX EVALUATION:
# ================
if analysis_mode == 'title':
ground_truth = item[item.find(' = ') + 3:item.rfind(' < ')]
if verbose and confidence < confidence_threshold:
print item[:item.rfind(' = ')]
print 'G:', ground_truth, '|| P:',
if results_to_csv:
title = item[:item.rfind(' = ')]
lineWriter.writerow([
title, ground_truth, chroma[0], chroma[1], chroma[2],
chroma[3], chroma[4], chroma[5], chroma[6], chroma[7],
chroma[8], chroma[9], chroma[10], chroma[11], chroma[12],
chroma[13], chroma[14], chroma[15], chroma[16], chroma[17],
chroma[18], chroma[19], chroma[20], chroma[21], chroma[22],
chroma[23], chroma[24], chroma[25], chroma[26], chroma[27],
chroma[28], chroma[29], chroma[30], chroma[31], chroma[32],
chroma[33], chroma[34], chroma[35], result
])
ground_truth = key_to_list(ground_truth)
estimation = key_to_list(result)
score = mirex_score(ground_truth, estimation)
mirex_scores.append(score)
else:
filename_to_match = item[:item.rfind('.')] + '.txt'
print filename_to_match
if filename_to_match in groundtruth_files:
groundtruth_file = open(
groundtruth_folder + '/' + filename_to_match, 'r')
ground_truth = groundtruth_file.readline()
if "\t" in ground_truth:
ground_truth = re.sub("\t", " ", ground_truth)
if results_to_csv:
lineWriter.writerow([
filename_to_match, chroma[0], chroma[1], chroma[2],
chroma[3], chroma[4], chroma[5], chroma[6], chroma[7],
chroma[8], chroma[9], chroma[10], chroma[11],
chroma[12], chroma[13], chroma[14], chroma[15],
chroma[16], chroma[17], chroma[18], chroma[19],
chroma[20], chroma[21], chroma[22], chroma[23],
chroma[24], chroma[25], chroma[26], chroma[27],
chroma[28], chroma[29], chroma[30], chroma[31],
chroma[32], chroma[33], chroma[34], chroma[35], result
])
ground_truth = key_to_list(ground_truth)
estimation = key_to_list(result)
score = mirex_score(ground_truth, estimation)
mirex_scores.append(score)
else:
print "FILE NOT FOUND... Skipping it from evaluation.\n"
continue
# CONFUSION MATRIX:
# ================
if confusion_matrix:
xpos = (ground_truth[0] +
(ground_truth[0] * 24)) + (-1 *
(ground_truth[1] - 1) * 24 * 12)
ypos = ((estimation[0] - ground_truth[0]) +
(-1 * (estimation[1] - 1) * 12))
matrix[(xpos + ypos)] = +matrix[(xpos + ypos)] + 1
if verbose and confidence < confidence_threshold:
print result, '(%.2f)' % confidence, '|| SCORE:', score, '\n'
# WRITE RESULTS TO FILE:
# =====================
if results_to_file:
with open(temp_folder + '/' + item[:-3] + 'txt', 'w') as textfile:
textfile.write(result)
textfile.close()
if results_to_csv:
csvFile.close()
print len(mirex_scores), "files analysed in", reloj(), "secs.\n"
if confusion_matrix:
matrix = np.matrix(matrix)
matrix = matrix.reshape(24, 24)
print matrix
if results_to_file:
np.savetxt(
temp_folder + '/_confusion_matrix.csv',
matrix,
fmt='%i',
delimiter=',',
header=
'C,C#,D,Eb,E,F,F#,G,G#,A,Bb,B,Cm,C#m,Dm,Ebm,Em,Fm,F#m,Gm,G#m,Am,Bbm,Bm'
)
# MIREX RESULTS
# =============
evaluation_results = mirex_evaluation(mirex_scores)
# WRITE INFO TO FILE
# ==================
if results_to_file:
settings = "SETTINGS\n========\nAvoid edges ('%' of duration disregarded at both ends (0 = complete)) = " + str(
avoid_edges
) + "\nfirst N secs = " + str(
first_n_secs
) + "\nshift spectrum to fit tempered scale = " + str(
shift_spectrum
) + "\nspectral whitening = " + str(
spectral_whitening
) + "\nsample rate = " + str(sample_rate) + "\nwindow size = " + str(
window_size
) + "\nhop size = " + str(hop_size) + "\nmagnitude threshold = " + str(
magnitude_threshold
) + "\nminimum frequency = " + str(
min_frequency
) + "\nmaximum frequency = " + str(
max_frequency
) + "\nmaximum peaks = " + str(max_peaks) + "\nband preset = " + str(
band_preset
) + "\nsplit frequency = " + str(
split_frequency
) + "\nharmonics = " + str(harmonics) + "\nnon linear = " + str(
non_linear
) + "\nnormalize = " + str(
normalize
) + "\nreference frequency = " + str(
reference_frequency
) + "\nhpcp size = " + str(
hpcp_size
) + "\nweigth type = " + weight_type + "\nweight window size in semitones = " + str(
weight_window_size
) + "\nharmonics key = " + str(num_harmonics) + "\nslope = " + str(
slope) + "\nprofile = " + profile_type + "\npolyphony = " + str(
use_polyphony) + "\nuse three chords = " + str(
use_three_chords)
results_for_file = "\n\nEVALUATION RESULTS\n==================\nCorrect: " + str(
evaluation_results[0]) + "\nFifth: " + str(
evaluation_results[1]) + "\nRelative: " + str(
evaluation_results[2]) + "\nParallel: " + str(
evaluation_results[3]) + "\nError: " + str(
evaluation_results[4]) + "\nWeighted: " + str(
evaluation_results[5])
write_to_file = open(temp_folder + '/_SUMMARY.txt', 'w')
write_to_file.write(settings)
write_to_file.write(results_for_file)
if analysis_mode == 'title':
corpus = "\n\nANALYSIS CORPUS\n===============\n" + str(
collection) + '\n' + str(
genre) + '\n' + str(modality) + '\n\n' + str(
len(mirex_scores)) + " files analysed.\n"
write_to_file.write(corpus)
write_to_file.close()
