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Linting scripts/plotly.streamlit.py

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This commit is contained in:
Jake Herbst
2022-05-24 12:32:40 -04:00
parent cc03d65315
commit 2330bd773a
1 changed files with 114 additions and 105 deletions
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scripts/plotly_streamlit.py
+114 -105
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@@ -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('<style>div.row-widget.stRadio > div{flex-direction:row;justify-content: left;} </style>', unsafe_allow_html=True)
st.write('<style>div.st-bf{flex-direction:column;} div.st-ag{font-weight:bold;padding-left:2px;}</style>', 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=('<b>Top '+ str(top_N) + ' Species in Date Range '+str(Date_Slider[0])+' to '+str(Date_Slider[1])+' 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))
)
rows=3, cols=2,
specs=[[{"type": "xy", "rowspan": 3}, {"type": "polar", "rowspan": 2}], [{"rowspan": 1}, {"rowspan": 1}], [None, {"type": "xy", "rowspan": 1}]],
subplot_titles=('<b>Top ' +
str(top_N) +
' Species in Date Range ' +
str(Date_Slider[0]) +
' to ' +
str(Date_Slider[1]) +
' 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)
)
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}: <br>Popularity: %{percent} </br> %{r}"
),
angularaxis = dict(
tickfont_size= font_size,
rotation = -90,
direction = 'clockwise',
polar=dict(
radialaxis=dict(
tickfont_size=font_size,
showticklabels=False,
hoverformat="#%{theta}: <br>Popularity: %{percent} </br> %{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}: <br>Popularity: %{percent} </br> %{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}: <br>Popularity: %{percent} </br> %{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=('<b>Daily Top '+ str(top_N) + ' Species in Date Range '+str(Date_Slider[0])+' to '+str(Date_Slider[1])+'</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)
subplot_titles=('<b>Daily Top ' + str(top_N) + ' Species in Date Range ' + str(Date_Slider[0]) + ' to ' + str(Date_Slider[1]) + '</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)
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')