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cleanup: remove commented-out and dead code

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frederik
2024-04-16 15:51:01 +02:00
parent 0d5eadb880
commit c0de584821
1 changed files with 0 additions and 67 deletions
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scripts/plotly_streamlit.py
-67
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@@ -190,8 +190,6 @@ def sunrise_sunset_scatter(date_range):
current_date = start_date
for current_date in date_range:
# current_date = datetime.fromisocalendar(2022, week + 1, 5)
# time_zone = datetime.now()
sun_rise = sun.get_local_sunrise_time(current_date)
sun_dusk = sun.get_local_sunset_time(current_date)
@@ -218,7 +216,6 @@ def sunrise_sunset_scatter(date_range):
def hms_to_dec(t):
# (h, m, s) = t.split(':')
h = t.hour
m = t.minute / 60
s = t.second / 3600
@@ -227,11 +224,8 @@ def hms_to_dec(t):
def hms_to_str(t):
# (h, m, s) = t.split(':')
h = t.hour
m = t.minute
# s = t.second / 3600
# result = h + m + s
return "%02d:%02d" % (h, m)
@@ -244,7 +238,6 @@ if daily is False:
species,
index=0)
# filt = df2['Com_Name'] == specie
if specie == 'All':
df_counts = int(hourly[hourly.index == specie]['All'].iloc[0])
fig = make_subplots(
@@ -255,10 +248,6 @@ if daily is False:
subplot_titles=('<b>Top ' + str(top_N) + ' Species in Date Range ' + str(start_date) + ' to ' + str(
end_date) + '<br>for ' + str(resample_sel) + ' sampling interval.' + '</b>',
'Total Detect:' + str('{:,}'.format(df_counts))
# + ' Confidence Max:' + str(
# '{:.2f}%'.format(max(df2[df2['Com_Name'] == specie]['Confidence']) * 100)) +
# ' ' + ' Median:' + str(
# '{:.2f}%'.format(np.median(df2[df2['Com_Name'] == specie]['Confidence']) * 100))
)
)
fig.layout.annotations[1].update(x=0.7, y=0.25, font_size=15)
@@ -380,29 +369,6 @@ if daily is False:
st.audio(userDir + '/BirdSongs/Extracted/By_Date/' + date_dir + '/' + specie_dir + '/' + recording)
except Exception:
st.title('RECORDING NOT AVAILABLE :(')
# try:
# con = sqlite3.connect(userDir + '/BirdNET-Pi/scripts/birds.db')
# cur = con.cursor()
cola, colb, colc, cold = st.columns((3, 1, 1, 1))
with colb:
seen = st.checkbox('Reviewed')
if seen:
with colc:
verified = st.radio("Verification", ['True Positive', 'False Positive'])
if verified == "False Positive":
df_names = pd.read_csv(userDir + '/BirdNET-Pi/model/labels.txt', delimiter='_', names=['Sci_Name', 'Com_Name'])
df_unknown = pd.DataFrame({"Sci_Name": ["UNKNOWN"], "Com_Name": ["UNKNOWN"]})
df_names = pd.concat([df_unknown, df_names], ignore_index=True)
with cold:
corrected = st.selectbox('What species?', df_names['Com_Name'])
# cur.execute("UPDATE detections SET Seen = seen WHERE File_Name = recording")
# con.commit()
# con.close()
# except BaseException:
# print("Database busy")
# time.sleep(2)
else:
@@ -411,22 +377,10 @@ if daily is False:
species[1:],
index=0)
# filt = df2[df2['Com_Name'] == specie]
df_counts = int(hourly[hourly.index == specie]['All'])
fig = st.container()
fig = make_subplots(rows=1, cols=1)
# specs= [[{"type":"xy","rowspan":1},{"type":"heatmap","rowspan":1}]],
# subplot_titles=('<b>Daily Top '+ str(top_N) + ' Species in Date Range '+ str(start_date) +' to '+ str(end_date) +'</b>',
# '<b>Daily ' + specie+ ' Detections on 15 minute intervals </b>'),
# # 'Total Detect:'+str('{:,}'.format(df_counts))+
# # ' Confidence Max:'+str('{:.2f}%'.format(max(df2[df2['Com_Name']==specie]['Confidence'])*100))+
# # ' '+' Median:'+str('{:.2f}%'.format(np.median(df2[df2['Com_Name']==specie]['Confidence'])*100))
# # )
# )
# fig.add_trace(go.Bar(y=top_N_species.index, x=top_N_species, orientation='h'), row=1,col=1)
df4 = df2['Com_Name'][df2['Com_Name'] == specie].resample('15min').count()
df4.index = [df4.index.date, df4.index.time]
day_hour_freq = df4.unstack().fillna(0)
@@ -437,21 +391,15 @@ if daily is False:
fig_y = [h.strftime('%H:%M') for h in day_hour_freq.columns.tolist()]
day_hour_freq.columns = fig_dec_y
fig_z = day_hour_freq.values.transpose()
# fig_heatmap = go.Figure(data=go.Heatmap(x=fig_x,y=fig_y,z=fig_z))
# fig.update_layout(
# margin=dict(l=0, r=0, t=50, b=0),
# yaxis={'categoryorder':'total ascending'})
color_pals = px.colors.named_colorscales()
selected_pal = st.sidebar.selectbox('Select Color Pallet for Daily Detections', color_pals)
heatmap = go.Heatmap(
# x=fig_x, y=fig_y,
x=fig_x,
y=day_hour_freq.columns,
z=fig_z, # heat.values,
showscale=False,
# text=labels,
texttemplate="%{text}", autocolorscale=False, colorscale=selected_pal
)
daysback_range, sunrise_list, sunrise_text_list = sunrise_sunset_scatter(day_hour_freq.index.tolist())
@@ -490,17 +438,9 @@ else:
plt_topN_today = (df6['Com_Name'].value_counts()[:readings])
freq_order = pd.value_counts(df6['Com_Name']).iloc[:readings].index
# confmax = df6.groupby('Com_Name')['Confidence'].max()
# reorder confmax to detection frequency order
# confmax = confmax.reindex(freq_order)
# norm = plt.Normalize(confmax.values.min(), confmax.values.max())
#
# colors = plt.cm.Greens(norm(confmax))
fig.add_trace(go.Bar(y=plt_topN_today.index, x=plt_topN_today, marker_color='seagreen', orientation='h'), row=1,
col=1)
# plot=sns.countplot(y='Com_Name', data = df_plt_topN_today, palette = colors, order=freq_order, ax=axs[0])
df6['Hour of Day'] = [r.hour for r in df6.index.time]
heat = pd.crosstab(df6['Com_Name'], df6['Hour of Day'])
# Order heatmap Birds by frequency of occurrance
@@ -525,15 +465,8 @@ else:
showgrid=True)
fig.update_layout(xaxis_ticks="inside",
margin=dict(l=0, r=0, t=50, b=0))
# container=st.container()
# config={'displayModelBar': False}
st.plotly_chart(fig, use_container_width=True) # , config=config)
# cols3,cols4=st.columns((1,1))
# extract_date=Date_Slider
# audio_file = open('/home/*/BirdSongs/Extracted/By_Date/2022-03-22/Yellow-streaked_Greenbul/Yellow-streaked_Greenbul-77-2022-03-22-birdnet-15:04:28.mp3', 'rb')
# audio_bytes = audio_file.read()
# cols4.audio(audio_bytes, format='audio/mp3')
if profile:
profiler.stop()
profiler.print()