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Updating script/daily_plot.py to satisfy pep8 style guide

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Used 'autopep8 --in-place --aggressive scripts/daily_plot.py' as initial style fixes.
This commit is contained in:
Jake Herbst
2022-05-11 07:56:41 -04:00
parent 26079e5696
commit 3e20ee732e
1 changed files with 138 additions and 71 deletions
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scripts/daily_plot.py
+138 -71
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@@ -12,80 +12,113 @@ userDir = os.path.expanduser('~')
conn = sqlite3.connect(userDir + '/BirdNET-Pi/scripts/birds.db') conn = sqlite3.connect(userDir + '/BirdNET-Pi/scripts/birds.db')
df = pd.read_sql_query("SELECT * from detections", conn) df = pd.read_sql_query("SELECT * from detections", conn)
cursor = conn.cursor() cursor = conn.cursor()
cursor.execute('SELECT * FROM detections WHERE Date = DATE(\'now\', \'localtime\')') cursor.execute(
'SELECT * FROM detections WHERE Date = DATE(\'now\', \'localtime\')')
table_rows = cursor.fetchall() table_rows = cursor.fetchall()
#df=pd.DataFrame(table_rows) # df=pd.DataFrame(table_rows)
#Convert Date and Time Fields to Panda's format # Convert Date and Time Fields to Panda's format
df['Date']=pd.to_datetime(df['Date']) df['Date'] = pd.to_datetime(df['Date'])
df['Time']=pd.to_datetime(df['Time'], unit='ns') df['Time'] = pd.to_datetime(df['Time'], unit='ns')
#Add round hours to dataframe # Add round hours to dataframe
df['Hour of Day'] = [r.hour for r in df.Time] df['Hour of Day'] = [r.hour for r in df.Time]
#Create separate dataframes for separate locations # Create separate dataframes for separate locations
df_plt=df #Default to use the whole Dbase df_plt = df # Default to use the whole Dbase
#Get todays readings # Get todays readings
now = datetime.now() now = datetime.now()
df_plt_today = df_plt[df_plt['Date']==now.strftime("%Y-%m-%d")] df_plt_today = df_plt[df_plt['Date'] == now.strftime("%Y-%m-%d")]
#Set number of species to report # Set number of species to report
readings=10 readings = 10
plt_top10_today = (df_plt_today['Com_Name'].value_counts()[:readings]) plt_top10_today = (df_plt_today['Com_Name'].value_counts()[:readings])
df_plt_top10_today = df_plt_today[df_plt_today.Com_Name.isin(plt_top10_today.index)] df_plt_top10_today = df_plt_today[df_plt_today.Com_Name.isin(
plt_top10_today.index)]
#Set Palette for graphics # Set Palette for graphics
pal = "Greens" pal = "Greens"
#Set up plot axes and titles # Set up plot axes and titles
f, axs = plt.subplots(1, 2, figsize = (10, 4), gridspec_kw=dict(width_ratios=[3, 6]), facecolor='#77C487') f, axs = plt.subplots(
plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0, hspace=0) 1, 2, figsize=(
10, 4), gridspec_kw=dict(
width_ratios=[
3, 6]), facecolor='#77C487')
plt.subplots_adjust(
left=None,
bottom=None,
right=None,
top=None,
wspace=0,
hspace=0)
#generate y-axis order for all figures based on frequency # generate y-axis order for all figures based on frequency
freq_order = pd.value_counts(df_plt_top10_today['Com_Name']).iloc[:readings].index freq_order = pd.value_counts(
df_plt_top10_today['Com_Name']).iloc[:readings].index
#make color for max confidence --> this groups by name and calculates max conf # make color for max confidence --> this groups by name and calculates max conf
confmax = df_plt_top10_today.groupby('Com_Name')['Confidence'].max() confmax = df_plt_top10_today.groupby('Com_Name')['Confidence'].max()
#reorder confmax to detection frequency order # reorder confmax to detection frequency order
confmax = confmax.reindex(freq_order) confmax = confmax.reindex(freq_order)
# norm values for color palette # norm values for color palette
norm = plt.Normalize(confmax.values.min(), confmax.values.max()) norm = plt.Normalize(confmax.values.min(), confmax.values.max())
colors = plt.cm.Greens(norm(confmax)) colors = plt.cm.Greens(norm(confmax))
#Generate frequency plot # Generate frequency plot
plot=sns.countplot(y='Com_Name', data = df_plt_top10_today, palette = colors, order=freq_order, ax=axs[0]) plot = sns.countplot(
y='Com_Name',
data=df_plt_top10_today,
palette=colors,
order=freq_order,
ax=axs[0])
# Try plot grid lines between bars - problem at the moment plots grid
# lines on bars - want between bars
#Try plot grid lines between bars - problem at the moment plots grid lines on bars - want between bars z = plot.get_ymajorticklabels()
z=plot.get_ymajorticklabels() plot.set_yticklabels(['\n'.join(textwrap.wrap(ticklabel.get_text(), 15))
plot.set_yticklabels(['\n'.join(textwrap.wrap(ticklabel.get_text(),15)) for ticklabel in plot.get_yticklabels()], fontsize = 10) for ticklabel in plot.get_yticklabels()], fontsize=10)
plot.set(ylabel=None) plot.set(ylabel=None)
plot.set(xlabel="Detections") plot.set(xlabel="Detections")
#Generate crosstab matrix for heatmap plot # Generate crosstab matrix for heatmap plot
heat = pd.crosstab(df_plt_top10_today['Com_Name'],df_plt_top10_today['Hour of Day']) heat = pd.crosstab(
#Order heatmap Birds by frequency of occurrance df_plt_top10_today['Com_Name'],
heat.index = pd.CategoricalIndex(heat.index, categories = freq_order) df_plt_top10_today['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.sort_index(level=0, inplace=True)
hours_in_day = pd.Series(data = range(0,24)) hours_in_day = pd.Series(data=range(0, 24))
heat_frame = pd.DataFrame(data=0, index=heat.index, columns = hours_in_day) heat_frame = pd.DataFrame(data=0, index=heat.index, columns=hours_in_day)
heat=(heat+heat_frame).fillna(0) heat = (heat + heat_frame).fillna(0)
#Generatie heatmap plot # Generatie heatmap plot
plot = sns.heatmap(heat, norm=LogNorm(), annot=True, annot_kws={"fontsize":7}, fmt="g", cmap = pal , square = False, cbar=False, linewidths = 0.5, linecolor = "Grey", ax=axs[1], yticklabels = False) plot = sns.heatmap(
plot.set_xticklabels(plot.get_xticklabels(), rotation = 0, size = 7) heat,
norm=LogNorm(),
annot=True,
annot_kws={
"fontsize": 7},
fmt="g",
cmap=pal,
square=False,
cbar=False,
linewidths=0.5,
linecolor="Grey",
ax=axs[1],
yticklabels=False)
plot.set_xticklabels(plot.get_xticklabels(), rotation=0, size=7)
# Set heatmap border # Set heatmap border
for _, spine in plot.spines.items(): for _, spine in plot.spines.items():
@@ -93,13 +126,14 @@ for _, spine in plot.spines.items():
plot.set(ylabel=None) plot.set(ylabel=None)
plot.set(xlabel="Hour of Day") plot.set(xlabel="Hour of Day")
#Set combined plot layout and titles # Set combined plot layout and titles
f.subplots_adjust(top=0.9) f.subplots_adjust(top=0.9)
plt.suptitle("Top 10 Last Updated: "+ str(now.strftime("%Y-%m-%d %H:%M"))) plt.suptitle("Top 10 Last Updated: " + str(now.strftime("%Y-%m-%d %H:%M")))
#Save combined plot # Save combined plot
userDir = os.path.expanduser('~') userDir = os.path.expanduser('~')
savename=userDir + '/BirdSongs/Extracted/Charts/Combo-'+str(now.strftime("%Y-%m-%d"))+'.png' savename = userDir + '/BirdSongs/Extracted/Charts/Combo-' + \
str(now.strftime("%Y-%m-%d")) + '.png'
plt.savefig(savename) plt.savefig(savename)
plt.show() plt.show()
plt.close() plt.close()
@@ -107,20 +141,32 @@ plt.close()
# Get Bottom detection frequency # Get Bottom detection frequency
plt_Bot10_today = (df_plt_today['Com_Name'].value_counts()[-readings:]) plt_Bot10_today = (df_plt_today['Com_Name'].value_counts()[-readings:])
df_plt_Bot10_today = df_plt_today[df_plt_today.Com_Name.isin(plt_Bot10_today.index)] df_plt_Bot10_today = df_plt_today[df_plt_today.Com_Name.isin(
plt_Bot10_today.index)]
#Set Palette for graphics # Set Palette for graphics
pal = "Reds" pal = "Reds"
#Set up plot axes and titles # Set up plot axes and titles
f, axs = plt.subplots(1, 2, figsize = (10, 4), gridspec_kw=dict(width_ratios=[3, 6]), facecolor='#77C487') f, axs = plt.subplots(
plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0, hspace=0) 1, 2, figsize=(
10, 4), gridspec_kw=dict(
width_ratios=[
3, 6]), facecolor='#77C487')
plt.subplots_adjust(
left=None,
bottom=None,
right=None,
top=None,
wspace=0,
hspace=0)
#generate y-axis order for all figures based on frequency # generate y-axis order for all figures based on frequency
freq_order = pd.value_counts(df_plt_Bot10_today['Com_Name']).iloc[-readings:].index freq_order = pd.value_counts(
df_plt_Bot10_today['Com_Name']).iloc[-readings:].index
#make color for max confidence --> this groups by name and calculates max conf # make color for max confidence --> this groups by name and calculates max conf
confmax = df_plt_Bot10_today.groupby('Com_Name')['Confidence'].max() confmax = df_plt_Bot10_today.groupby('Com_Name')['Confidence'].max()
confmax = confmax.reindex(freq_order) confmax = confmax.reindex(freq_order)
# probably wrong order . . . how to sort by no. of detections ? # probably wrong order . . . how to sort by no. of detections ?
@@ -128,33 +174,53 @@ confmax = confmax.reindex(freq_order)
norm = plt.Normalize(confmax.values.min(), confmax.values.max()) norm = plt.Normalize(confmax.values.min(), confmax.values.max())
colors = plt.cm.Reds(norm(confmax)) colors = plt.cm.Reds(norm(confmax))
#Generate frequency plot # Generate frequency plot
plot=sns.countplot(y='Com_Name', data = df_plt_Bot10_today, palette = colors, order=freq_order, ax=axs[0]) plot = sns.countplot(
y='Com_Name',
data=df_plt_Bot10_today,
palette=colors,
order=freq_order,
ax=axs[0])
# Try plot grid lines between bars - problem at the moment plots grid
# lines on bars - want between bars
#Try plot grid lines between bars - problem at the moment plots grid lines on bars - want between bars z = plot.get_ymajorticklabels()
z=plot.get_ymajorticklabels() plot.set_yticklabels(['\n'.join(textwrap.wrap(ticklabel.get_text(), 15))
plot.set_yticklabels(['\n'.join(textwrap.wrap(ticklabel.get_text(),15)) for ticklabel in plot.get_yticklabels()], fontsize = 10) for ticklabel in plot.get_yticklabels()], fontsize=10)
plot.set(ylabel=None) plot.set(ylabel=None)
plot.set(xlabel="Detections") plot.set(xlabel="Detections")
#Generate crosstab matrix for heatmap plot # Generate crosstab matrix for heatmap plot
heat = pd.crosstab(df_plt_Bot10_today['Com_Name'],df_plt_Bot10_today['Hour of Day']) heat = pd.crosstab(
#Order heatmap Birds by frequency of occurrance df_plt_Bot10_today['Com_Name'],
heat.index = pd.CategoricalIndex(heat.index, categories = freq_order) df_plt_Bot10_today['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.sort_index(level=0, inplace=True)
hours_in_day = pd.Series(data = range(0,24)) hours_in_day = pd.Series(data=range(0, 24))
heat_frame = pd.DataFrame(data=0, index=heat.index, columns = hours_in_day) heat_frame = pd.DataFrame(data=0, index=heat.index, columns=hours_in_day)
heat=(heat+heat_frame).fillna(0) heat = (heat + heat_frame).fillna(0)
#Generatie heatmap plot # Generatie heatmap plot
plot = sns.heatmap(heat, norm=LogNorm(), annot=True, fmt="g", annot_kws={"fontsize":7}, cmap = pal , square = False, cbar=False, linewidths = 0.5, linecolor = "Grey", ax=axs[1], yticklabels = False) plot = sns.heatmap(
plot.set_xticklabels(plot.get_xticklabels(), rotation = 0, size = 7) heat,
norm=LogNorm(),
annot=True,
fmt="g",
annot_kws={
"fontsize": 7},
cmap=pal,
square=False,
cbar=False,
linewidths=0.5,
linecolor="Grey",
ax=axs[1],
yticklabels=False)
plot.set_xticklabels(plot.get_xticklabels(), rotation=0, size=7)
# Set heatmap border # Set heatmap border
for _, spine in plot.spines.items(): for _, spine in plot.spines.items():
@@ -162,12 +228,13 @@ for _, spine in plot.spines.items():
plot.set(ylabel=None) plot.set(ylabel=None)
plot.set(xlabel="Hour of Day") plot.set(xlabel="Hour of Day")
#Set combined plot layout and titles # Set combined plot layout and titles
f.subplots_adjust(top=0.9) f.subplots_adjust(top=0.9)
plt.suptitle("Bottom 10 Last Updated: "+ str(now.strftime("%Y-%m-%d %H:%M"))) plt.suptitle("Bottom 10 Last Updated: " + str(now.strftime("%Y-%m-%d %H:%M")))
#Save combined plot # Save combined plot
savename=userDir + '/BirdSongs/Extracted/Charts/Combo2-'+str(now.strftime("%Y-%m-%d"))+'.png' savename = userDir + '/BirdSongs/Extracted/Charts/Combo2-' + \
str(now.strftime("%Y-%m-%d")) + '.png'
plt.savefig(savename) plt.savefig(savename)
plt.show() plt.show()
plt.close() plt.close()