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Added log scale functionality to visualizations #24

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Feb 28, 2021
Merged

Added log scale functionality to visualizations #24

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131 changes: 87 additions & 44 deletions Microfaune_Local_Score_Package_Tutorial.ipynb
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12 changes: 8 additions & 4 deletions microfaune_local_score.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,6 +14,7 @@
from scipy.io import wavfile
import scipy.signal as scipy_signal
import pandas as pd
import math

# Gabriel's original moment-to-moment classification tool. Reworked to output
# a Pandas DataFrame.
Expand Down Expand Up @@ -278,7 +279,7 @@ def calc_local_scores_simpler(bird_dir,weight_path=None, Normalized_Sample_Rate
return annotations

# Function that produces graphs with the local score plot and spectrogram of an audio clip. Now integrated with Pandas so you can visualize human and automated annotations.
def local_line_graph(local_scores,clip_name, sample_rate,samples, automated_df=None, human_df=None, save_fig = False):
def local_line_graph(local_scores,clip_name, sample_rate,samples, automated_df=None, human_df=None,log_scale = False, save_fig = False):
# Calculating the length of the audio clip
duration = samples.shape[0]/sample_rate
# Calculating the number of local scores outputted by Microfaune
Expand All @@ -299,7 +300,10 @@ def local_line_graph(local_scores,clip_name, sample_rate,samples, automated_df=N
# score line plot - top plot
axs[0].plot(time_stamps, local_scores)
axs[0].set_xlim(0,duration)
axs[0].set_ylim(0,1)
if log_scale:
axs[0].set_yscale('log')
else:
axs[0].set_ylim(0,1)
axs[0].grid(which='major', linestyle='-')
# Adding in the optional automated labels from a Pandas DataFrame
if automated_df.empty == False:
Expand Down Expand Up @@ -335,7 +339,7 @@ def local_line_graph(local_scores,clip_name, sample_rate,samples, automated_df=N
# Wrapper function for the local_line_graph function for ease of use.
# TODO rework function so that instead of generating the automated labels, it takes the automated_df as input
# same as it does with the manual dataframe.
def local_score_visualization(clip_path,weight_path = None, human_df = None,automated_df = False, save_fig = False):
def local_score_visualization(clip_path,weight_path = None, human_df = None,automated_df = False,log_scale = False, save_fig = False):

# Loading in the clip with Microfaune's built-in loading function
SAMPLE_RATE, SIGNAL = audio.load_wav(clip_path)
Expand Down Expand Up @@ -372,7 +376,7 @@ def local_score_visualization(clip_path,weight_path = None, human_df = None,auto
else:
automated_df = pd.DataFrame()

local_line_graph(local_score[0].tolist(),clip_path,SAMPLE_RATE,SIGNAL,automated_df,human_df, save_fig = save_fig)
local_line_graph(local_score[0].tolist(),clip_path,SAMPLE_RATE,SIGNAL,automated_df,human_df,log_scale = log_scale, save_fig = save_fig)
## TODO: figure out why the local score plot is having the thresholded values set to 1 propagate to this function
def local_score_visualization2(clip_path,weight_path = None, human_df = None,automated_df = False, save_fig = False):

Expand Down