def delete_files(self):
'''
This function deletes all waveform data .csv
when convert_all is run
NO INPUTS OR OUTPUTS
'''
folder = Globals.getCsvDir()
for the_file in os.listdir(folder):
file_path = os.path.join(folder, the_file)
try:
if os.path.isfile(file_path):
os.unlink(file_path)
except Exception, e:
print e
raise
def delete_files(self):
'''
This function deletes all waveform data .csv
when convert_all is run
NO INPUTS OR OUTPUTS
'''
folder = Globals.getCsvDir()
for the_file in os.listdir(folder):
file_path = os.path.join(folder, the_file)
try:
if os.path.isfile(file_path):
os.unlink(file_path)
except Exception, e:
print e
raise
def write_data_to_csv(self, song_data, song_name, genre, column_labels):
'''
This function writes the song's feature row data
into a csv file for modeling
INPUT: song_data = 1d numpy array of floats containing
song feature data; song_name (string); genre (string);
column_labels = 1d numpy array of strings, song feature labels
OUTPUT: NONE
'''
csv_dir = Globals.getCsvDir()
song_data = song_data.tolist()
song_data.append(song_name)
song_data.append(genre)
column_names = [str(lab) for lab in column_labels]
column_names.append("Song Name")
column_names.append("Label")
with open(csv_dir + "\\" + song_name + '.csv', 'wb') as csvfile:
writer = csv.writer(csvfile, delimiter=',')
writer.writerow(column_names)
writer.writerow(song_data)
def write_data_to_csv(self, song_data, song_name, genre, column_labels):
'''
This function writes the song's feature row data
into a csv file for modeling
INPUT: song_data = 1d numpy array of floats containing
song feature data; song_name (string); genre (string);
column_labels = 1d numpy array of strings, song feature labels
OUTPUT: NONE
'''
csv_dir = Globals.getCsvDir()
song_data = song_data.tolist()
song_data.append(song_name)
song_data.append(genre)
column_names = [str(lab) for lab in column_labels]
column_names.append("Song Name")
column_names.append("Label")
with open(csv_dir + "\\" + song_name + '.csv', 'wb') as csvfile:
writer = csv.writer(csvfile, delimiter=',')
writer.writerow(column_names)
writer.writerow(song_data)
def df_stitcher(self):
dir = Globals.getCsvDir()
'''
This loads all of the outputted csv data from
convert_all into the pandas data frame for analysis.
It returns just the pandas data frame
INPUT: none
OUTPUT: pandas data frame containing feature data
'''
'''
this could also go into the Modeler file/class
'''
file_list = [f for f in listdir(dir) if isfile(join(dir, f))]
df = pd.DataFrame()
for file_name in file_list:
try:
df_dum = pd.read_csv(dir + "\\" + file_name)
except:
continue
df = df.append(df_dum, ignore_index=True)
df = df.dropna()
return df
def df_stitcher(self):
dir = Globals.getCsvDir()
'''
This loads all of the outputted csv data from
convert_all into the pandas data frame for analysis.
It returns just the pandas data frame
INPUT: none
OUTPUT: pandas data frame containing feature data
'''
'''
this could also go into the Modeler file/class
'''
file_list = [f for f in listdir(dir) if isfile(join(dir, f))]
df = pd.DataFrame()
for file_name in file_list:
try:
df_dum = pd.read_csv(dir + "\\" + file_name)
except:
continue
df = df.append(df_dum, ignore_index=True)
df = df.dropna()
return df