Merge branch 'Nachtzuster:main' into patch-7

README.md

@@ -197,6 +197,11 @@ I hope that if you find BirdNET-Pi has been worth your time, you will share your
 BirdNET-Pi can also be run as a [Homeassistant](https://www.home-assistant.io/) addon through docker.
 For more information : https://github.com/alexbelgium/hassio-addons/blob/master/birdnet-pi/README.md
 
+## Docker
+
+BirdNET-Pi can also be run as as a docker container.
+For more information : https://github.com/alexbelgium/hassio-addons/blob/master/birdnet-pi/README_standalone.md
+
 ## Cool Links
 
 - [Marie Lelouche's <i>Out of Spaces</i>](https://www.lestanneries.fr/exposition/marie-lelouche-out-of-spaces/) using BirdNET-Pi in post-sculpture VR! [Press Kit](https://github.com/mcguirepr89/BirdNET-Pi-assets/blob/main/dp_out_of_spaces_marie_lelouche_digital_05_01_22.pdf)

scripts/daily_plot.py

@@ -84,7 +84,7 @@ def create_plot(df_plt_today, now, is_top=None):
     if conf['COLOR_SCHEME'] == "dark":
         facecolor = 'darkgrey'
     else:
-        facecolor = 'none'
+        facecolor = '#77C487'
 
     f, axs = plt.subplots(1, 2, figsize=(10, height), gridspec_kw=dict(width_ratios=[3, 6]), facecolor=facecolor)
 

scripts/plotly_streamlit.py

@@ -55,7 +55,8 @@
 
 @st.cache_resource()
 def get_connection(path: str):
-    return sqlite3.connect(path, check_same_thread=False)
+    uri = f"file:{path}?mode=ro"
+    return sqlite3.connect(uri, uri=True, check_same_thread=False)
 
 
 def get_data(_conn: Connection):
@@ -256,24 +257,24 @@ def hms_to_str(t):
 
             # 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', marker_color='seagreen'), row=1, col=1)
+            fig.add_trace(go.Bar(y=top_N_species.index.tolist(), x=top_N_species.values.tolist(), orientation='h', marker_color='seagreen'), row=1, col=1)
 
             fig.update_layout(
                 margin=dict(l=0, r=0, t=50, b=0),
                 yaxis={'categoryorder': 'total ascending'})
 
             # Set 360 degrees, 24 hours for polar plot
-            theta = np.linspace(0.0, 360, 24, endpoint=False)
+            theta = np.linspace(0.0, 360, 24, endpoint=False).tolist()
 
             specie_filt = df5 == specie
             df3 = df5[specie_filt]
 
             detections2 = pd.crosstab(df3, df3.index.hour)
 
-            d = pd.DataFrame(np.zeros((23, 1))).squeeze()
+            d = pd.DataFrame(np.zeros((24, 1))).squeeze()
             detections = hourly.loc[specie]
             detections = (d + detections).fillna(0)
-            fig.add_trace(go.Barpolar(r=detections, theta=theta, marker_color='seagreen'), row=1, col=2)
+            fig.add_trace(go.Barpolar(r=detections.tolist(), theta=theta, marker_color='seagreen'), row=1, col=2)
             fig.update_layout(
                 autosize=False,
                 width=1000,
@@ -301,7 +302,7 @@ def hms_to_str(t):
             )
 
             daily = pd.crosstab(df5, df5.index.date, dropna=True, margins=True)
-            fig.add_trace(go.Bar(x=daily.columns[:-1], y=daily.loc[specie][:-1], marker_color='seagreen'), row=3, col=2)
+            fig.add_trace(go.Bar(x=daily.columns[:-1].tolist(), y=daily.loc[specie][:-1].tolist(), marker_color='seagreen'), row=3, col=2)
             st.plotly_chart(fig, use_container_width=True)  # , config=config)
 
         else:
@@ -312,17 +313,17 @@ def hms_to_str(t):
                     specs=[[{"type": "polar", "rowspan": 2}], [{"rowspan": 1}], [{"type": "xy", "rowspan": 1}]]
                 )
                 # Set 360 degrees, 24 hours for polar plot
-                theta = np.linspace(0.0, 360, 24, endpoint=False)
+                theta = np.linspace(0.0, 360, 24, endpoint=False).tolist()
 
                 specie_filt = df5 == specie
                 df3 = df5[specie_filt]
 
                 detections2 = pd.crosstab(df3, df3.index.hour)
 
-                d = pd.DataFrame(np.zeros((23, 1))).squeeze()
+                d = pd.DataFrame(np.zeros((24, 1))).squeeze()
                 detections = hourly.loc[specie]
                 detections = (d + detections).fillna(0)
-                fig.add_trace(go.Barpolar(r=detections, theta=theta, marker_color='seagreen'), row=1, col=1)
+                fig.add_trace(go.Barpolar(r=detections.tolist(), theta=theta, marker_color='seagreen'), row=1, col=1)
                 fig.update_layout(
                     autosize=False,
                     width=1000,
@@ -350,7 +351,7 @@ def hms_to_str(t):
                 )
 
                 daily = pd.crosstab(df5, df5.index.date, dropna=True, margins=True)
-                fig.add_trace(go.Bar(x=daily.columns[:-1], y=daily.loc[specie][:-1], marker_color='seagreen'), row=3, col=1)
+                fig.add_trace(go.Bar(x=daily.columns[:-1].tolist(), y=daily.loc[specie][:-1].tolist(), marker_color='seagreen'), row=3, col=1)
                 st.plotly_chart(fig, use_container_width=True)  # , config=config)
                 df_counts = int(hourly[hourly.index == specie]['All'].iloc[0])
                 st.subheader('Total Detect:' + str('{:,}'.format(df_counts))
@@ -379,8 +380,8 @@ def hms_to_str(t):
                               species[1:],
                               index=0)
 
-        df_counts = int(hourly[hourly.index == specie]['All'])
-        fig = st.container()
+        df_counts = int(hourly.loc[hourly.index == specie, 'All'].iloc[0])
+
         fig = make_subplots(rows=1, cols=1)
 
         df4 = df2['Com_Name'][df2['Com_Name'] == specie].resample('15min').count()
@@ -392,14 +393,14 @@ def hms_to_str(t):
         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()]
         day_hour_freq.columns = fig_dec_y
-        fig_z = day_hour_freq.values.transpose()
+        fig_z = day_hour_freq.values.transpose().tolist()
 
         color_pals = px.colors.named_colorscales()
         selected_pal = st.sidebar.selectbox('Select Color Pallet for Daily Detections', color_pals)
 
         heatmap = go.Heatmap(
             x=fig_x,
-            y=day_hour_freq.columns,
+            y=day_hour_freq.columns.tolist(),
             z=fig_z,  # heat.values,
             showscale=False,
             texttemplate="%{text}", autocolorscale=False, colorscale=selected_pal
@@ -440,7 +441,7 @@ def hms_to_str(t):
 
     plt_topN_today = (df6['Com_Name'].value_counts()[:readings])
     freq_order = pd.value_counts(df6['Com_Name']).iloc[:readings].index
-    fig.add_trace(go.Bar(y=plt_topN_today.index, x=plt_topN_today, marker_color='seagreen', orientation='h'), row=1,
+    fig.add_trace(go.Bar(y=plt_topN_today.index.tolist(), x=plt_topN_today.values.tolist(), marker_color='seagreen', orientation='h'), row=1,
                   col=1)
 
     df6['Hour of Day'] = [r.hour for r in df6.index.time]
@@ -449,6 +450,7 @@ def hms_to_str(t):
     heat.index = pd.CategoricalIndex(heat.index, categories=freq_order)
     heat.sort_index(level=0, inplace=True)
 
+    heat.index = heat.index.astype(str)
     heat_plot_values = ma.log(heat.values).filled(0)
 
     hours_in_day = pd.Series(data=range(0, 24))
@@ -459,7 +461,7 @@ def hms_to_str(t):
 
     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,
+    fig.add_trace(go.Heatmap(x=heat.columns.tolist(), y=heat.index.tolist(), z=heat_values_normalized,  # heat.values,
                              showscale=False,
                              text=labels, texttemplate="%{text}", colorscale='Blugrn'
                              ), row=1, col=2)