<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
 You can use the single line call "analyze," which does all the available analysis simultaneously
Step1:  ... or call all the methods individually
<ASSISTANT_TASK:>
Python Code:
# NOTE: This will take several minutes depending on the performance of your machine
audio_features = audioAnalyzer.analyze(audio_filename)
# plot the features
plt.rcParams['figure.figsize'] = [20, 8]
audioAnalyzer.plot(audio_features)
plt.show()
Explanation: You can use the single line call "analyze," which does all the available analysis simultaneously
End of explanation
# audio metadata extraction
metadata = audioAnalyzer.crawl_musicbrainz_metadata(audio_filename)
# predominant melody extraction
pitch = audioAnalyzer.extract_pitch(audio_filename)
# pitch post filtering
pitch_filtered = audioAnalyzer.filter_pitch(pitch)
# histogram computation
pitch_distribution = audioAnalyzer.compute_pitch_distribution(pitch_filtered)
pitch_class_distribution = copy.deepcopy(pitch_distribution)
pitch_class_distribution.to_pcd()
# tonic identification
tonic = audioAnalyzer.identify_tonic(pitch_filtered)
# get the makam from metadata if possible else apply makam recognition
makams = audioAnalyzer.get_makams(metadata, pitch_filtered, tonic)
makam = list(makams)[0]  # for now get the first makam
# transposition (ahenk) identification
transposition = audioAnalyzer.identify_transposition(tonic, makam)
# stable note extraction (tuning analysis)
note_models = audioAnalyzer.compute_note_models(pitch_distribution, tonic, makam)
# get the melodic progression model
melodic_progression = audioAnalyzer.compute_melodic_progression(pitch_filtered)
Explanation: ... or call all the methods individually
End of explanation
<END_TASK>