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README.md

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----
-title: AlaskaLightning
-emoji: 🌍
-colorFrom: yellow
-colorTo: green
-sdk: streamlit
-sdk_version: 1.35.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import streamlit as st
+import geopandas as gpd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import pandas as pd
+from io import BytesIO
+import requests
+import folium
+import zipfile
+from streamlit.components.v1 import html
+
+# Función para descargar y descomprimir el archivo
+def download_and_extract_data():
+    url = 'https://fire.ak.blm.gov/content/maps/aicc/Data/Data%20(zipped%20Shapefiles)/CurrentYearLightning_SHP.zip'
+    response = requests.get(url)
+    zip_file = zipfile.ZipFile(BytesIO(response.content))
+    zip_file.extractall("CurrentYearLightning_SHP")
+
+# Descargar y descomprimir los datos
+st.title('Alaska Lightning Detection Network Analysis')
+
+st.subheader('How its works: Explorar y comprender mejor la actividad de los rayos. ')
+
+with st.spinner('Downloading and extracting data...'):
+    download_and_extract_data()
+
+
+# Cargar el shapefile
+shapefile_path = 'CurrentYearLightning_SHP/TOA_STRIKES.shp'
+alaskaP = gpd.read_file(shapefile_path)
+
+st.subheader('Current Year Lightning Strikes in Alaska')
+m = folium.Map(location=[alaskaP['LATITUDE'].mean(), alaskaP['LONGITUDE'].mean()], zoom_start=6)
+
+for _, row in alaskaP.iterrows():
+    folium.CircleMarker(
+        location=[row['LATITUDE'], row['LONGITUDE']],
+        radius=3,
+        weight=1,
+        color='red',
+        fill=True,
+        fill_color='red'
+    ).add_to(m)
+
+# Renderizar el mapa usando st.components.v1.html
+folium_html = m._repr_html_()
+html(folium_html, width=700, height=500)
+
+
+# Convertir a datetime
+alaskaP['STRIKETIME'] = pd.to_datetime(alaskaP['STRIKETIME'])
+
+alaskaP['year'] = alaskaP['STRIKETIME'].dt.year
+alaskaP['month'] = alaskaP['STRIKETIME'].dt.month
+alaskaP['day'] = alaskaP['STRIKETIME'].dt.day
+alaskaP['hour'] = alaskaP['STRIKETIME'].dt.hour
+alaskaP['dayofweek'] = alaskaP['STRIKETIME'].dt.dayofweek
+alaskaP['week'] = alaskaP['STRIKETIME'].dt.isocalendar().week
+
+# Número de rayos por mes
+st.subheader('Number of Lightning Strikes by Month')
+fig, ax = plt.subplots()
+sns.countplot(x='month', data=alaskaP, ax=ax)
+ax.set_title('Number of Lightning Strikes by Month')
+ax.set_xlabel('Month')
+ax.set_ylabel('Count')
+st.pyplot(fig)
+
+# Número de rayos por día
+st.subheader('Number of Lightning Strikes by Day')
+fig, ax = plt.subplots()
+sns.countplot(x='day', data=alaskaP, ax=ax)
+ax.set_title('Number of Lightning Strikes by Day')
+ax.set_xlabel('Day')
+ax.set_ylabel('Count')
+st.pyplot(fig)
+
+# Distribución de tipos de rayos en un mapa
+st.subheader('Lightning Strikes by Type')
+fig, ax = plt.subplots()
+alaskaP.plot(column="STROKETYPE", legend=True, ax=ax)
+ax.set_title('Lightning Strikes by Type')
+st.pyplot(fig)
+
+
+# Número de rayos por día del año actual
+st.subheader('Number of Lightning Strikes by Day in Current Year')
+alaskaP['day'] = alaskaP['STRIKETIME'].dt.date
+daily_counts = alaskaP['day'].value_counts().sort_index()
+daily_counts.index = pd.to_datetime(daily_counts.index)
+fig, ax = plt.subplots()
+sns.lineplot(x=daily_counts.index, y=daily_counts.values, marker='o', ax=ax)
+ax.set_title('Number of Lightning Strikes by Day')
+ax.set_xlabel('Date')
+ax.set_ylabel('Count')
+ax.grid(True)
+st.pyplot(fig)
+
+# Número de rayos por hora en el año actual
+st.subheader('Number of Lightning Strikes by Hour in Current Year')
+current_year = alaskaP['year'].max()
+daily_data = alaskaP[alaskaP['year'] == current_year]
+hourly_counts = daily_data['hour'].value_counts().sort_index()
+fig, ax = plt.subplots()
+sns.lineplot(x=hourly_counts.index, y=hourly_counts.values, ax=ax)
+ax.set_title('Number of Lightning Strikes by Hour in Current Year')
+ax.set_xlabel('Hour of the Day')
+ax.set_ylabel('Count')
+ax.grid(True)
+st.pyplot(fig)
+
+# Número de rayos por día de la semana
+st.subheader('Number of Lightning Strikes by Day of the Week')
+alaskaP['day_of_week'] = alaskaP['STRIKETIME'].dt.dayofweek
+day_of_week_counts = alaskaP['day_of_week'].value_counts().sort_index()
+fig, ax = plt.subplots()
+sns.barplot(x=day_of_week_counts.index, y=day_of_week_counts.values, ax=ax)
+ax.set_title('Number of Lightning Strikes by Day of the Week')
+ax.set_xlabel('Day of the Week')
+ax.set_ylabel('Count')
+ax.set_xticklabels(['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'])
+ax.grid(True)
+st.pyplot(fig)
+
+# Número de rayos por día en los últimos 10 días
+st.subheader('Number of Lightning Strikes by Day in Last 10 Days')
+last_10_days = daily_counts.tail(10)
+fig, ax = plt.subplots()
+sns.lineplot(x=last_10_days.index, y=last_10_days.values, marker='o', ax=ax)
+ax.set_title('Number of Lightning Strikes by Day in Last 10 Days')
+ax.set_xlabel('Date')
+ax.set_ylabel('Count')
+ax.grid(True)
+st.pyplot(fig)

requirements.txt

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+streamlit
+geopandas
+matplotlib
+seaborn
+pandas
+requests
+folium 
+matplotlib 
+mapclassify
+streamlit-folium