diff --git a/scripts/plotly_streamlit.py b/scripts/plotly_streamlit.py index 2d7eb6a..2df1550 100755 --- a/scripts/plotly_streamlit.py +++ b/scripts/plotly_streamlit.py @@ -4,8 +4,7 @@ import pandas as pd import numpy as np import plotly.graph_objects as go from plotly.subplots import make_subplots -from datetime import timedelta, datetime -from pathlib import Path +from datetime import timedelta import sqlite3 from sqlite3 import Connection import plotly.express as px @@ -35,22 +34,22 @@ st.markdown(""" @st.cache(hash_funcs={Connection: id}) -def get_connection(path:str): - return sqlite3.connect(path,check_same_thread=False) +def get_connection(path: str): + return sqlite3.connect(path, check_same_thread=False) def get_data(conn: Connection): - df1=pd.read_sql("SELECT * FROM detections", con=conn) + df1 = pd.read_sql("SELECT * FROM detections", con=conn) return df1 + conn = get_connection(URI_SQLITE_DB) # Read in the cereal data # df = load_data() -df=get_data(conn) -df2=df.copy() -df2['DateTime']=pd.to_datetime(df2['Date'] + " " + df2['Time']) -df2=df2.set_index('DateTime') - +df = get_data(conn) +df2 = df.copy() +df2['DateTime'] = pd.to_datetime(df2['Date'] + " " + df2['Time']) +df2 = df2.set_index('DateTime') # Filter on date range @@ -60,174 +59,184 @@ df2=df2.set_index('DateTime') # Date as slider Start_Date = pd.to_datetime(df2.index.min()).date() -End_Date = pd.to_datetime(df2.index.max()).date() -cols1,cols2= st.columns((1,1)) +End_Date = pd.to_datetime(df2.index.max()).date() +cols1, cols2 = st.columns((1, 1)) Date_Slider = cols1.slider('Date Range', - min_value = Start_Date-timedelta(days=1), - max_value = End_Date, - value=(Start_Date, - End_Date) - ) + min_value=Start_Date - timedelta(days=1), + max_value=End_Date, + value=(Start_Date, + End_Date) + ) - -filt = (df2.index >= pd.Timestamp(Date_Slider[0])) & (df2.index <= pd.Timestamp(Date_Slider[1]+timedelta(days=1))) +filt = (df2.index >= pd.Timestamp(Date_Slider[0])) & (df2.index <= pd.Timestamp(Date_Slider[1] + timedelta(days=1))) df2 = df2[filt] st.write('', unsafe_allow_html=True) st.write('', unsafe_allow_html=True) -resample_sel=cols2.radio("Select Resample Resolution - To downsample and make run faster select longer period, Daily provides a view on detections at 15 min intervals through the day", ('1 minute', '5 minutes', '10 minutes', 'Hourly', 'Daily')) +resample_sel = cols2.radio( + ''' + Select Resample Resolution - To downsample and make run faster select longer period, + Daily provides a view on detections at 15 min intervals through the day + ''', + ('1 minute', + '5 minutes', + '10 minutes', + 'Hourly', + 'Daily')) -resample_times = {'1 minute':'1min', - '5 minutes':'5min', - '10 minutes':'10min', - 'Hourly':'1H', - 'Daily':'1D' +resample_times = {'1 minute': '1min', + '5 minutes': '5min', + '10 minutes': '10min', + 'Hourly': '1H', + 'Daily': '1D' } resample_time = resample_times[resample_sel] -df5=df2.resample(resample_time)['Com_Name'].aggregate('unique').explode() +df5 = df2.resample(resample_time)['Com_Name'].aggregate('unique').explode() -#Create species count for selected date range +# Create species count for selected date range -Specie_Count=df5.value_counts() +Specie_Count = df5.value_counts() -#Create species treemap +# Create species treemap # Create Hourly Crosstab -hourly=pd.crosstab(df5,df5.index.hour, dropna=False) +hourly = pd.crosstab(df5, df5.index.hour, dropna=False) # Filter on species species = list(hourly.index) -cols1,cols2= st.columns((1,1)) +cols1, cols2 = st.columns((1, 1)) top_N = cols1.slider( 'Select Number of Birds to Show', - min_value = 1, - value=min(10,len(Specie_Count)) - ) + min_value=1, + value=min(10, len(Specie_Count)) +) top_N_species = (df5.value_counts()[:top_N]) -specie = cols2.selectbox('Which bird would you like to explore for the dates '+str(Date_Slider[0])+' to '+str(Date_Slider[1])+'?', species, - index=species.index(list(top_N_species.index)[0])) +specie = cols2.selectbox('Which bird would you like to explore for the dates ' + str(Date_Slider[0]) + ' to ' + str(Date_Slider[1]) + '?', species, + index=species.index(list(top_N_species.index)[0])) -font_size=15 +font_size = 15 -#specie filter -filt=df2['Com_Name']==specie - -df_counts=sum(df5==specie) - +# specie filter +filt = df2['Com_Name'] == specie +df_counts = sum(df5 == specie) if resample_time != '1D': fig = make_subplots( - rows=3, cols =2, - specs= [[{"type":"xy","rowspan":3}, {"type":"polar","rowspan":2}], [{"rowspan":1}, {"rowspan":1} ], [None, {"type":"xy","rowspan":1}]], - subplot_titles=('Top '+ str(top_N) + ' Species in Date Range '+str(Date_Slider[0])+' to '+str(Date_Slider[1])+' for '+str(resample_sel)+' sampling interval.'+'', - '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)) - ) + rows=3, cols=2, + specs=[[{"type": "xy", "rowspan": 3}, {"type": "polar", "rowspan": 2}], [{"rowspan": 1}, {"rowspan": 1}], [None, {"type": "xy", "rowspan": 1}]], + subplot_titles=('Top ' + + str(top_N) + + ' Species in Date Range ' + + str(Date_Slider[0]) + + ' to ' + + str(Date_Slider[1]) + + ' for ' + + str(resample_sel) + + ' sampling interval.' + + '', + '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) + ) + fig.layout.annotations[1].update(x=0.7, y=0.25, font_size=15) - #Plot seen species for selected date range and number of species - fig.add_trace(go.Bar(y=top_N_species.index, x=top_N_species, orientation='h'), row=1,col=1) + # Plot seen species for selected date range and number of species + fig.add_trace(go.Bar(y=top_N_species.index, x=top_N_species, orientation='h'), row=1, col=1) fig.update_layout( margin=dict(l=0, r=0, t=50, b=0), - yaxis={'categoryorder':'total ascending'}) - + yaxis={'categoryorder': 'total ascending'}) # Set 360 degrees, 24 hours for polar plot theta = np.linspace(0.0, 360, 24, endpoint=False) - specie_filt= df5==specie - df3=df5[specie_filt] + specie_filt = df5 == specie + df3 = df5[specie_filt] - detections2= pd.crosstab(df3, df3.index.hour) + detections2 = pd.crosstab(df3, df3.index.hour) - - - - d=pd.DataFrame(np.zeros((23,1))).squeeze() + d = pd.DataFrame(np.zeros((23, 1))).squeeze() detections = hourly.loc[specie] - detections=(d+detections).fillna(0) - fig.add_trace(go.Barpolar(r = detections, theta=theta), row=1, col=2) + detections = (d + detections).fillna(0) + fig.add_trace(go.Barpolar(r=detections, theta=theta), row=1, col=2) fig.update_layout( autosize=False, - width = 1000, - height = 500, + width=1000, + height=500, showlegend=False, - polar = dict( - radialaxis = dict( - tickfont_size = font_size, - showticklabels = False, - hoverformat = "#%{theta}:
Popularity: %{percent}
%{r}" - ), - angularaxis = dict( - tickfont_size= font_size, - rotation = -90, - direction = 'clockwise', + polar=dict( + radialaxis=dict( + tickfont_size=font_size, + showticklabels=False, + hoverformat="#%{theta}:
Popularity: %{percent}
%{r}" + ), + angularaxis=dict( + tickfont_size=font_size, + rotation=-90, + direction='clockwise', tickmode='array', - tickvals=[0,15,35,45,60,75,90,105,120,135,150,165,180,195,210,225,240,255,270,285,300,315,330,345], - ticktext=['12am','1am','2am','3am','4am','5am', '6am','7am','8am','9am','10am','11am','12pm','1pm','2pm','3pm','4pm','5pm', '6pm','7pm','8pm','9pm','10pm','11pm'], - hoverformat = "#%{theta}:
Popularity: %{percent}
%{r}" + tickvals=[0, 15, 35, 45, 60, 75, 90, 105, 120, 135, 150, 165, 180, 195, 210, 225, 240, 255, 270, 285, 300, 315, 330, 345], + ticktext=['12am', '1am', '2am', '3am', '4am', '5am', '6am', '7am', '8am', '9am', '10am', '11am', + '12pm', '1pm', '2pm', '3pm', '4pm', '5pm', '6pm', '7pm', '8pm', '9pm', '10pm', '11pm'], + hoverformat="#%{theta}:
Popularity: %{percent}
%{r}" ), - ), - ) + ), + ) - - - daily=pd.crosstab(df5,df5.index.date, dropna=False) + daily = pd.crosstab(df5, df5.index.date, dropna=False) fig.add_trace(go.Bar(x=daily.columns, y=daily.loc[specie]), row=3, col=2) else: fig = make_subplots( - rows=1, cols =2, - specs= [[{"type":"xy","rowspan":1},{"type":"xy","rowspan":1}]], - + rows=1, cols=2, + specs=[[{"type": "xy", "rowspan": 1}, {"type": "xy", "rowspan": 1}]], - subplot_titles=('Daily Top '+ str(top_N) + ' Species in Date Range '+str(Date_Slider[0])+' to '+str(Date_Slider[1])+'', - 'Daily ' + specie+ ' Detections on 15 minute intervals '), -# '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) + subplot_titles=('Daily Top ' + str(top_N) + ' Species in Date Range ' + str(Date_Slider[0]) + ' to ' + str(Date_Slider[1]) + '', + 'Daily ' + specie + ' Detections on 15 minute intervals '), + # '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) fig_x = [d.strftime('%d-%m-%Y') for d in day_hour_freq.index.tolist()] fig_y = [h.strftime('%H:%M') for h in day_hour_freq.columns.tolist()] fig_z = day_hour_freq.values.transpose() - fig_heatmap = go.Figure(data=go.Heatmap(x=fig_x,y=fig_y,z=fig_z)) + 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() + margin=dict(l=0, r=0, t=50, b=0), + yaxis={'categoryorder': 'total ascending'}) + color_pals = px.colors.named_colorscales() selected_pal = cols2.selectbox('Select Color Pallet for Daily Detections', color_pals) - fig.add_trace(go.Heatmap(x=fig_x,y=fig_y,z=fig_z, autocolorscale = False, colorscale = selected_pal), row=1, col=2) + fig.add_trace(go.Heatmap(x=fig_x, y=fig_y, z=fig_z, autocolorscale=False, colorscale=selected_pal), row=1, col=2) # container=st.container() # config={'displayModelBar': False} -st.plotly_chart(fig, use_container_width=True) #, config=config) +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')