Updating script/plotly_streamlit.py to correct pep8 violations
Used 'autopep8 --in-place --aggressive scripts/plotly_streamlit.py' as initial style fixes.
This commit is contained in:
Jake Herbst
+68
-66
@@ -34,22 +34,22 @@ st.markdown("""
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@st.cache(hash_funcs={Connection: id})
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def get_connection(path:str):
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return sqlite3.connect(path,check_same_thread=False)
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def get_connection(path: str):
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return sqlite3.connect(path, check_same_thread=False)
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def get_data(conn: Connection):
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df1=pd.read_sql("SELECT * FROM detections", con=conn)
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df1 = pd.read_sql("SELECT * FROM detections", con=conn)
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return df1
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conn = get_connection(URI_SQLITE_DB)
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# Read in the cereal data
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# df = load_data()
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df=get_data(conn)
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df2=df.copy()
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df2['DateTime']=pd.to_datetime(df2['Date'] + " " + df2['Time'])
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df2=df2.set_index('DateTime')
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df = get_data(conn)
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df2 = df.copy()
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df2['DateTime'] = pd.to_datetime(df2['Date'] + " " + df2['Time'])
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df2 = df2.set_index('DateTime')
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# Filter on date range
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@@ -59,115 +59,117 @@ df2=df2.set_index('DateTime')
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# Date as slider
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Start_Date = pd.to_datetime(df2.index.min()).date()
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End_Date = pd.to_datetime(df2.index.max()).date()
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End_Date = pd.to_datetime(df2.index.max()).date()
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Date_Slider = st.slider('Date Range',
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min_value = Start_Date-timedelta(days=1),
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max_value = End_Date,
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value=(Start_Date,
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End_Date)
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)
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min_value=Start_Date - timedelta(days=1),
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max_value=End_Date,
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value=(Start_Date,
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End_Date)
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)
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filt = (df2.index >= pd.Timestamp(Date_Slider[0])) & (df2.index <= pd.Timestamp(Date_Slider[1]+timedelta(days=1)))
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filt = (df2.index >= pd.Timestamp(Date_Slider[0])) & (df2.index <= pd.Timestamp(Date_Slider[1] + timedelta(days=1)))
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df2 = df2[filt]
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#Create species count for selected date range
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# Create species count for selected date range
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Specie_Count=df2['Com_Name'].value_counts()
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Specie_Count = df2['Com_Name'].value_counts()
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#Create species treemap
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# Create species treemap
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# Create Hourly Crosstab
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hourly=pd.crosstab(df2['Com_Name'],df2.index.hour, dropna=False)
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hourly = pd.crosstab(df2['Com_Name'], df2.index.hour, dropna=False)
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# Filter on species
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species = list(hourly.index)
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cols1,cols2= st.columns((1,1))
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cols1, cols2 = st.columns((1, 1))
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top_N = cols1.slider(
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'Select Number of Birds to Show',
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min_value = 1,
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value=min(10,len(Specie_Count))
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)
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min_value=1,
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value=min(10, len(Specie_Count))
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)
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top_N_species = (df2['Com_Name'].value_counts()[:top_N])
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specie = cols2.selectbox('Which bird would you like to explore for the dates '+str(Date_Slider[0])+' to '+str(Date_Slider[1])+'?', species,
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index=species.index(list(top_N_species.index)[0]))
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specie = cols2.selectbox('Which bird would you like to explore for the dates ' + str(Date_Slider[0]) + ' to ' + str(Date_Slider[1]) + '?', species,
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index=species.index(list(top_N_species.index)[0]))
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font_size=15
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font_size = 15
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#specie filter
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filt=df2['Com_Name']==specie
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# specie filter
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filt = df2['Com_Name'] == specie
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df_counts=df2[filt].resample('D').count()
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df_counts = df2[filt].resample('D').count()
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fig = make_subplots(
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rows=3, cols =2,
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specs= [[{"type":"xy","rowspan":3}, {"type":"polar","rowspan":2}], [{"rowspan":1}, {"rowspan":1} ], [None, {"type":"xy","rowspan":1}]],
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subplot_titles=('<b>Top '+ str(top_N) + ' Species in Date Range '+str(Date_Slider[0])+' to '+str(Date_Slider[1])+'</b>',
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'Total Detect:'+str('{:,}'.format(sum(df_counts.Time)))+
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' Confidence Max:'+str('{:.2f}%'.format(max(df2[df2['Com_Name']==specie]['Confidence'])*100))+
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' '+' Median:'+str('{:.2f}%'.format(np.median(df2[df2['Com_Name']==specie]['Confidence'])*100))
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)
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rows=3, cols=2,
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specs=[[{"type": "xy", "rowspan": 3}, {"type": "polar", "rowspan": 2}], [
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{"rowspan": 1}, {"rowspan": 1}], [None, {"type": "xy", "rowspan": 1}]],
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subplot_titles=('<b>Top ' + str(top_N) + ' Species in Date Range ' + str(Date_Slider[0]) + ' to ' + str(Date_Slider[1]) + '</b>',
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'Total Detect:' + str('{:,}'.format(sum(df_counts.Time))) +
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' Confidence Max:' + str('{:.2f}%'.format(max(df2[df2['Com_Name'] == specie]['Confidence']) * 100)) +
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' ' + ' Median:' +
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str('{:.2f}%'.format(np.median(df2[df2['Com_Name'] == specie]['Confidence']) * 100))
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)
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fig.layout.annotations[1].update(x=0.7,y=0.25, font_size=15)
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)
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fig.layout.annotations[1].update(x=0.7, y=0.25, font_size=15)
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#Plot seen species for selected date range and number of species
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fig.add_trace(go.Bar(y=top_N_species.index, x=top_N_species, orientation='h'), row=1,col=1)
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# Plot seen species for selected date range and number of species
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fig.add_trace(go.Bar(y=top_N_species.index, x=top_N_species, orientation='h'), row=1, col=1)
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fig.update_layout(
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margin=dict(l=0, r=0, t=50, b=0),
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yaxis={'categoryorder':'total ascending'})
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yaxis={'categoryorder': 'total ascending'})
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# Set 360 degrees, 24 hours for polar plot
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theta = np.linspace(0.0, 360, 24, endpoint=False)
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d=pd.DataFrame(np.zeros((23,1))).squeeze()
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d = pd.DataFrame(np.zeros((23, 1))).squeeze()
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detections = hourly.loc[specie]
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detections=(d+detections).fillna(0)
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fig.add_trace(go.Barpolar(r = detections, theta=theta), row=1, col=2)
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detections = (d + detections).fillna(0)
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fig.add_trace(go.Barpolar(r=detections, theta=theta), row=1, col=2)
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fig.update_layout(
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autosize=False,
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width = 1000,
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height = 500,
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width=1000,
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height=500,
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showlegend=False,
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polar = dict(
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radialaxis = dict(
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tickfont_size = font_size,
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showticklabels = True,
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hoverformat = "#%{theta}: <br>Popularity: %{percent} </br> %{r}"
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),
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angularaxis = dict(
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tickfont_size= font_size,
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rotation = -90,
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direction = 'clockwise',
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polar=dict(
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radialaxis=dict(
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tickfont_size=font_size,
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showticklabels=True,
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hoverformat="#%{theta}: <br>Popularity: %{percent} </br> %{r}"
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),
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angularaxis=dict(
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tickfont_size=font_size,
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rotation=-90,
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direction='clockwise',
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tickmode='array',
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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],
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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'],
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hoverformat = "#%{theta}: <br>Popularity: %{percent} </br> %{r}"
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tickvals=[0, 15, 35, 45, 60, 75, 90, 105, 120, 135, 150, 165,
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180, 195, 210, 225, 240, 255, 270, 285, 300, 315, 330, 345],
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ticktext=['12am', '1am', '2am', '3am', '4am', '5am', '6am', '7am', '8am', '9am', '10am', '11am',
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'12pm', '1pm', '2pm', '3pm', '4pm', '5pm', '6pm', '7pm', '8pm', '9pm', '10pm', '11pm'],
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hoverformat="#%{theta}: <br>Popularity: %{percent} </br> %{r}"
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),
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),
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)
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),
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)
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daily=pd.crosstab(df2['Com_Name'],df2.index.date, dropna=False)
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daily = pd.crosstab(df2['Com_Name'], df2.index.date, dropna=False)
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fig.add_trace(go.Bar(x=daily.columns, y=daily.loc[specie]), row=3, col=2)
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# container=st.container()
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# config={'displayModelBar': False}
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st.plotly_chart(fig, use_container_width=True) #, config=config)
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st.plotly_chart(fig, use_container_width=True) # , config=config)
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# cols3,cols4=st.columns((1,1))
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#
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#
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# extract_date=Date_Slider
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#
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#
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# 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')
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# audio_bytes = audio_file.read()
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# cols4.audio(audio_bytes, format='audio/mp3')
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