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Updating script/plotly_streamlit.py to correct pep8 violations

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Used 'autopep8 --in-place --aggressive scripts/plotly_streamlit.py' as initial style fixes.
This commit is contained in:
Jake Herbst
2022-05-11 08:23:42 -04:00
parent 9b12feea5d
commit 7ad6bbe93e
1 changed files with 68 additions and 66 deletions
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scripts/plotly_streamlit.py
+9 -7
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@@ -42,6 +42,7 @@ 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 return df1
conn = get_connection(URI_SQLITE_DB) conn = get_connection(URI_SQLITE_DB)
# Read in the cereal data # Read in the cereal data
# df = load_data() # df = load_data()
@@ -51,7 +52,6 @@ df2['DateTime']=pd.to_datetime(df2['Date'] + " " + df2['Time'])
df2 = df2.set_index('DateTime') df2 = df2.set_index('DateTime')
# Filter on date range # Filter on date range
# Date as calendars # Date as calendars
# Start_Date = pd.to_datetime(st.sidebar.date_input('Which date do you want to start?', value = df2.index.min())) # Start_Date = pd.to_datetime(st.sidebar.date_input('Which date do you want to start?', value = df2.index.min()))
@@ -68,7 +68,6 @@ Date_Slider = st.slider('Date Range',
) )
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] df2 = df2[filt]
@@ -108,11 +107,13 @@ df_counts=df2[filt].resample('D').count()
fig = make_subplots( fig = make_subplots(
rows=3, cols=2, rows=3, cols=2,
specs= [[{"type":"xy","rowspan":3}, {"type":"polar","rowspan":2}], [{"rowspan":1}, {"rowspan":1} ], [None, {"type":"xy","rowspan":1}]], 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]) + '</b>', subplot_titles=('<b>Top ' + str(top_N) + ' Species in Date Range ' + str(Date_Slider[0]) + ' to ' + str(Date_Slider[1]) + '</b>',
'Total Detect:' + str('{:,}'.format(sum(df_counts.Time))) + 'Total Detect:' + str('{:,}'.format(sum(df_counts.Time))) +
' Confidence Max:' + str('{:.2f}%'.format(max(df2[df2['Com_Name'] == specie]['Confidence']) * 100)) + ' 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)) ' ' + ' 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)
@@ -147,15 +148,16 @@ fig.update_layout(
rotation=-90, rotation=-90,
direction='clockwise', direction='clockwise',
tickmode='array', 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], tickvals=[0, 15, 35, 45, 60, 75, 90, 105, 120, 135, 150, 165,
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'], 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}" hoverformat="#%{theta}: <br>Popularity: %{percent} </br> %{r}"
), ),
), ),
) )
daily = pd.crosstab(df2['Com_Name'], df2.index.date, dropna=False) daily = pd.crosstab(df2['Com_Name'], df2.index.date, dropna=False)
fig.add_trace(go.Bar(x=daily.columns, y=daily.loc[specie]), row=3, col=2) fig.add_trace(go.Bar(x=daily.columns, y=daily.loc[specie]), row=3, col=2)