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Linting scripts/daily_plot.py

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Jake Herbst
2022-05-24 12:30:15 -04:00
parent 01456ce583
commit cc03d65315
1 changed files with 87 additions and 66 deletions
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scripts/daily_plot.py
+87 -66
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@@ -1,6 +1,5 @@
import sqlite3 import sqlite3
import os import os
import configparser
import pandas as pd import pandas as pd
import seaborn as sns import seaborn as sns
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
@@ -18,84 +17,95 @@ 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
# Add every font at the specified location # Add every font at the specified location
font_dir = [userDir+'/BirdNET-Pi/homepage/static'] font_dir = [userDir + '/BirdNET-Pi/homepage/static']
for font in font_manager.findSystemFonts(font_dir): for font in font_manager.findSystemFonts(font_dir):
font_manager.fontManager.addfont(font) font_manager.fontManager.addfont(font)
# Set font family globally # Set font family globally
rcParams['font.family'] = 'Roboto Flex' rcParams['font.family'] = 'Roboto Flex'
#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(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) 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
z = plot.get_ymajorticklabels()
#Try plot grid lines between bars - problem at the moment plots grid lines on bars - want between bars plot.set_yticklabels(['\n'.join(textwrap.wrap(ticklabel.get_text(), 15)) for ticklabel in plot.get_yticklabels()], fontsize=10)
z=plot.get_ymajorticklabels()
plot.set_yticklabels(['\n'.join(textwrap.wrap(ticklabel.get_text(),15)) 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(df_plt_top10_today['Com_Name'], df_plt_top10_today['Hour of Day'])
#Order heatmap Birds by frequency of occurrance # Order heatmap Birds by frequency of occurrance
heat.index = pd.CategoricalIndex(heat.index, categories = freq_order) 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():
@@ -103,13 +113,13 @@ 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("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()
@@ -119,18 +129,18 @@ plt.close()
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(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) 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 ?
@@ -138,33 +148,44 @@ 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
z = plot.get_ymajorticklabels()
#Try plot grid lines between bars - problem at the moment plots grid lines on bars - want between bars plot.set_yticklabels(['\n'.join(textwrap.wrap(ticklabel.get_text(), 15)) for ticklabel in plot.get_yticklabels()], fontsize=10)
z=plot.get_ymajorticklabels()
plot.set_yticklabels(['\n'.join(textwrap.wrap(ticklabel.get_text(),15)) 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(df_plt_Bot10_today['Com_Name'], df_plt_Bot10_today['Hour of Day'])
#Order heatmap Birds by frequency of occurrance # Order heatmap Birds by frequency of occurrance
heat.index = pd.CategoricalIndex(heat.index, categories = freq_order) 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():
@@ -172,12 +193,12 @@ 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()