Updated Species Stats with some cleanup and requests
This commit is contained in:
CaiusX
+230
-135
@@ -2,19 +2,25 @@ import os
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import streamlit as st
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import pandas as pd
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import numpy as np
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from numpy import ma
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import plotly.graph_objects as go
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from plotly.subplots import make_subplots
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from datetime import timedelta
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import plotly.io as pio
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from datetime import timedelta, datetime
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from pathlib import Path
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import sqlite3
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from sqlite3 import Connection
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import plotly.express as px
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from sklearn.preprocessing import normalize
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pio.templates.default = "plotly_white"
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userDir = os.path.expanduser('~')
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URI_SQLITE_DB = userDir + '/BirdNET-Pi/scripts/birds.db'
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st.set_page_config(layout='wide')
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# Remove whitespace from the top of the page and sidebar
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# Remove whitespace from the top of the page
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st.markdown("""
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<style>
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.css-18e3th9 {
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@@ -33,7 +39,9 @@ st.markdown("""
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""", unsafe_allow_html=True)
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@st.cache(hash_funcs={Connection: id})
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# @st.cache(hash_funcs={Connection: id})
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@st.cache(allow_output_mutation=True)
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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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@@ -44,199 +52,286 @@ def get_data(conn: Connection):
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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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daily = st.sidebar.checkbox('Single Day View', help= 'Select if you want single day view, unselect for multi-day views')
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# Filter on date range
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# Date as calendars
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# Start_Date = pd.to_datetime(st.sidebar.date_input('Which date do you want to start?', value = df2.index.min()))
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# End_Date = pd.to_datetime(st.sidebar.date_input('Which date do you want to end?', value = df2.index.max()))
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if daily:
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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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cols1, cols2 = st.columns((1, 1))
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Date_Slider = cols1.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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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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# cols1, cols2 = st.columns((1, 1))
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end_date = st.sidebar.slider('Date to View',
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min_value = Start_Date,
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max_value = End_Date,
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value=(End_Date),
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help= 'Select date for single day view'
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)
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start_date = end_date
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else:
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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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# cols1, cols2 = st.columns((1, 1))
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start_date, end_date = st.sidebar.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, End_Date),
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help= 'Select start and end date, if same date get a clockplot for a single day'
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)
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# start_date, end_date = cols1.date_input(
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# "Date Input for Analysis - select Range for single specie analysis, select single date for daily view",
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# value=(Start_Date, End_Date),
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# min_value=Start_Date,
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# max_value=End_Date)
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# start_date = datetime(2022 ,5 ,17).date()
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# end_date = datetime(2022 ,5 ,17).date()
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@st.cache()
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def date_filter(df, start_date, end_date):
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filt = (df2.index >= pd.Timestamp(start_date)) & (df2.index <= pd.Timestamp(end_date + timedelta(days=1)))
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df = df[filt]
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return(df)
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df2 = date_filter(df2, start_date, end_date)
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st.write('<style>div.row-widget.stRadio > div{flex-direction:row;justify-content: left;} </style>',
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unsafe_allow_html=True)
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st.write('<style>div.st-bf{flex-direction:column;} div.st-ag{font-weight:bold;padding-left:2px;}</style>',
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unsafe_allow_html=True)
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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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# Select time period buttons
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# Disallow "Daily time period" for "Daily Chart"
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if start_date == end_date:
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resample_sel = st.sidebar.radio(
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"Resample Resolution",
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('Raw', '15 minutes', 'Hourly'), index=1, help= 'Select resolution for single day - larger times run faster' )
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st.write('<style>div.row-widget.stRadio > div{flex-direction:row;justify-content: left;} </style>', unsafe_allow_html=True)
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st.write('<style>div.st-bf{flex-direction:column;} div.st-ag{font-weight:bold;padding-left:2px;}</style>', unsafe_allow_html=True)
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resample_times = {'Raw': 'Raw',
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'1 minute': '1min',
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'15 minutes': '15min',
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'Hourly': '1H'
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}
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resample_time = resample_times[resample_sel]
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resample_sel = cols2.radio(
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'''
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Select Resample Resolution - To downsample and make run faster select longer period,
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Daily provides a view on detections at 15 min intervals through the day
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''',
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('1 minute',
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'5 minutes',
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'10 minutes',
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'Hourly',
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'Daily'))
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else:
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resample_sel = st.sidebar.radio(
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"Resample Resolution",
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('Raw', '15 minutes', 'Hourly', 'DAILY'), index=1, help= 'Select resolution for species - DAILY provides time series')
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resample_times = {'1 minute': '1min',
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'5 minutes': '5min',
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'10 minutes': '10min',
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'Hourly': '1H',
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'Daily': '1D'
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}
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resample_time = resample_times[resample_sel]
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resample_times = {'Raw': 'Raw',
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'1 minute': '1min',
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'15 minutes': '15min',
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'Hourly': '1H',
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'DAILY': '1D'
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}
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resample_time = resample_times[resample_sel]
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df5 = df2.resample(resample_time)['Com_Name'].aggregate('unique').explode()
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@st.cache()
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def time_resample(df, resample_time):
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if resample_time == 'Raw':
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df_resample = df['Com_Name']
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else:
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df_resample = df.resample(resample_time)['Com_Name'].aggregate('unique').explode()
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return(df_resample)
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top_bird = df2['Com_Name'].mode()[0]
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df5 = time_resample(df2, resample_time)
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# Create species count for selected date range
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Specie_Count = df5.value_counts()
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# Create species treemap
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# Create Hourly Crosstab
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hourly = pd.crosstab(df5, df5.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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top_N = cols1.slider(
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#cols1, cols2 = st.columns((1, 1))
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top_N = st.sidebar.slider(
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'Select Number of Birds to Show',
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min_value=1,
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max_value=len(Specie_Count),
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value=min(10, len(Specie_Count))
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)
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top_N_species = (df5.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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font_size = 15
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if daily == False:
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specie = st.selectbox(
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'Which bird would you like to explore for the dates '
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+ str(start_date) + ' to ' + str(end_date) + '?',
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species,
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index = species.index(top_bird))
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# specie filter
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filt = df2['Com_Name'] == specie
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filt = df2['Com_Name'] == specie
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df_counts = sum(df5 == specie)
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df_counts = sum(df5 == specie)
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if resample_time != '1D':
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if resample_time != '1D':
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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 ' +
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str(top_N) +
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' Species in Date Range ' +
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str(Date_Slider[0]) +
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' to ' +
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str(Date_Slider[1]) +
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' for ' +
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str(resample_sel) +
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' sampling interval.' +
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'</b>',
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'Total Detect:' + str('{:,}'.format(df_counts)) +
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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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)
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fig.layout.annotations[1].update(x=0.7, y=0.25, font_size=15)
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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}],
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[None, {"type": "xy", "rowspan": 1}]],
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subplot_titles=('<b>Top ' + str(top_N) + ' Species in Date Range ' + str(start_date) + ' to ' + str(
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end_date) + ' for ' + str(resample_sel) + ' sampling interval.' + '</b>',
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'Total Detect:' + str('{:,}'.format(df_counts)) +
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' Confidence Max:' + str(
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'{:.2f}%'.format(max(df2[df2['Com_Name'] == specie]['Confidence']) * 100)) +
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' ' + ' Median:' + str(
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'{:.2f}%'.format(np.median(df2[df2['Com_Name'] == specie]['Confidence']) * 100))
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)
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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', marker_color='seagreen'), 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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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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# 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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# 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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specie_filt = df5 == specie
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df3 = df5[specie_filt]
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specie_filt = df5 == specie
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df3 = df5[specie_filt]
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detections2 = pd.crosstab(df3, df3.index.hour)
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detections2 = pd.crosstab(df3, df3.index.hour)
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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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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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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=False,
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hoverformat="#%{theta}: <br>Popularity: %{percent} </br> %{r}"
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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, marker_color='seagreen'), 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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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=False,
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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,
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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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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',
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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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daily = pd.crosstab(df5, df5.index.date, dropna=False)
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daily = pd.crosstab(df5, df5.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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fig.add_trace(go.Bar(x=daily.columns, y=daily.loc[specie], marker_color='seagreen'), row=3, col=2)
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else:
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fig = st.container()
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fig = make_subplots(
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rows=1, cols =1)
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# specs= [[{"type":"xy","rowspan":1},{"type":"heatmap","rowspan":1}]],
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# subplot_titles=('<b>Daily Top '+ str(top_N) + ' Species in Date Range '+ str(start_date) +' to '+ str(end_date) +'</b>',
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# '<b>Daily ' + specie+ ' Detections on 15 minute intervals </b>'),
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# # 'Total Detect:'+str('{:,}'.format(df_counts))+
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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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# )
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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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df4=df2['Com_Name'][df2['Com_Name']==specie].resample('15min').count()
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df4.index=[df4.index.date, df4.index.time]
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day_hour_freq=df4.unstack().fillna(0)
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fig_x = [d.strftime('%d-%m-%Y') for d in day_hour_freq.index.tolist()]
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fig_y = [h.strftime('%H:%M') for h in day_hour_freq.columns.tolist()]
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fig_z = day_hour_freq.values.transpose()
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# fig_heatmap = go.Figure(data=go.Heatmap(x=fig_x,y=fig_y,z=fig_z))
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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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color_pals= px.colors.named_colorscales()
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selected_pal = st.sidebar.selectbox('Select Color Pallet for Daily Detections', color_pals)
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fig.add_trace(go.Heatmap(x=fig_x,y=fig_y,z=fig_z, autocolorscale = False, colorscale = selected_pal), row=1, col=1)
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else:
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fig = make_subplots(
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rows=1, cols=2,
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specs=[[{"type": "xy", "rowspan": 1}, {"type": "xy", "rowspan": 1}]],
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subplot_titles=('<b>Daily Top ' + str(top_N) + ' Species in Date Range ' + str(Date_Slider[0]) + ' to ' + str(Date_Slider[1]) + '</b>',
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'<b>Daily ' + specie + ' Detections on 15 minute intervals </b>'),
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# 'Total Detect:'+str('{:,}'.format(df_counts))+
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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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subplot_titles=('<b>Top ' + str(top_N) + ' Species For ' + str(start_date) + '</b>',
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'<b>Daily ' + str(start_date) + ' Detections on ' + resample_sel + ' interval</b>'),
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shared_yaxes='all',
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horizontal_spacing=0
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)
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|
||||
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)
|
||||
df6 = df5.to_frame(name='Com_Name')
|
||||
readings = top_N
|
||||
|
||||
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))
|
||||
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)
|
||||
|
||||
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 = 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)
|
||||
# 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
|
||||
heat.index = pd.CategoricalIndex(heat.index, categories=freq_order)
|
||||
heat.sort_index(level=0, inplace=True)
|
||||
|
||||
heat_plot_values = ma.log(heat.values).filled(0)
|
||||
|
||||
hours_in_day = pd.Series(data=range(0, 24))
|
||||
heat_frame = pd.DataFrame(data=0, index=heat.index, columns=hours_in_day)
|
||||
|
||||
heat = (heat + heat_frame).fillna(0)
|
||||
heat_values_normalized = normalize(heat.values, axis=1, norm='l1')
|
||||
|
||||
labels = heat.values.astype(int).astype('str')
|
||||
labels[labels == '0'] = ""
|
||||
fig.add_trace(go.Heatmap(x=heat.columns, y=heat.index, z=heat_values_normalized, # heat.values,
|
||||
showscale=False,
|
||||
text=labels, texttemplate="%{text}", colorscale='Blugrn'
|
||||
), row=1, col=2)
|
||||
fig.update_yaxes(visible=True, autorange="reversed", ticks="inside", tickson="boundaries", ticklen=10000,
|
||||
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')
|
||||
|
||||
Reference in New Issue
Block a user