Commit
·
ce18646
1
Parent(s):
4000a79
Add application file
Browse files- app.py +214 -0
- pfm_Python_finale.ipynb +0 -0
- scopus_data_All.csv +0 -0
- scopus_data_all_cleaned.csv +0 -0
app.py
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import streamlit as st
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import pandas as pd
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import matplotlib.pyplot as plt
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from io import BytesIO
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from reportlab.lib.pagesizes import letter
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from reportlab.pdfgen import canvas
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from reportlab.lib.utils import ImageReader
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# Fonction pour générer un rapport PDF
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def generate_pdf(data, filtered_data, years, keywords, author):
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buffer = BytesIO()
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c = canvas.Canvas(buffer, pagesize=letter)
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width, height = letter
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# Titre du rapport
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c.setFont("Helvetica-Bold", 18)
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c.drawString(100, height - 40, "Rapport d'Analyse des Publications Scientifiques")
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# Information sur les filtres
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c.setFont("Helvetica", 12)
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filter_info = f"Filtres appliqués:\n- Années: {', '.join(map(str, years))}\n- Mots-clés: {', '.join(keywords)}"
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if author:
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filter_info += f"\n- Auteur: {author}"
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text_lines = filter_info.split('\n')
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y_position = height - 80
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for line in text_lines:
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c.drawString(50, y_position, line)
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y_position -= 20
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# Statistiques Générales
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total_publications = len(filtered_data)
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total_citations = filtered_data['Citation Count'].sum()
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avg_citations_per_publication = filtered_data['Citation Count'].mean()
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top_cited_publication = filtered_data.loc[filtered_data['Citation Count'].idxmax()]
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stats = [
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f"Nombre total de publications: {total_publications}",
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f"Nombre total de citations: {total_citations}",
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f"Citations moyennes par publication: {avg_citations_per_publication:.2f}",
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f"Publication avec le plus de citations: {top_cited_publication['Title']} ({top_cited_publication['Citation Count']} citations)"
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]
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c.drawString(50, y_position - 20, "Statistiques Générales:")
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for i, stat in enumerate(stats):
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c.drawString(70, y_position - 40 - 20 * i, stat)
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# Génération des visualisations
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plots = [
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("Distribution des Citations par Publication", lambda ax: ax.hist(filtered_data['Citation Count'], bins=20, color='skyblue', edgecolor='black')),
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("Citations par Année", lambda ax: ax.plot(filtered_data.groupby('Year')['Citation Count'].sum().reset_index()['Year'], filtered_data.groupby('Year')['Citation Count'].sum().reset_index()['Citation Count'], marker='o', color='skyblue')),
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("Nombre de Publications par Mot-Clé", lambda ax: ax.pie(filtered_data['Keyword'].value_counts(), labels=filtered_data['Keyword'].value_counts().index, autopct='%1.1f%%', colors=plt.cm.Paired(range(len(filtered_data['Keyword'].value_counts()))))),
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("Nombre de Publications par Année", lambda ax: ax.plot(filtered_data.groupby('Year').size().reset_index(name='Nombre de Publications')['Year'], filtered_data.groupby('Year').size().reset_index(name='Nombre de Publications')['Nombre de Publications'], marker='o', color='skyblue')),
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("Auteurs les Plus Cités", lambda ax: ax.bar(filtered_data.groupby('Authors')['Citation Count'].sum().reset_index().sort_values(by='Citation Count', ascending=False).head(10)['Authors'], filtered_data.groupby('Authors')['Citation Count'].sum().reset_index().sort_values(by='Citation Count', ascending=False).head(10)['Citation Count'], color='skyblue', edgecolor='black')),
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("Sujets les Plus Publiés", lambda ax: ax.bar(filtered_data['Keyword'].value_counts().head(10).index, filtered_data['Keyword'].value_counts().head(10), color='skyblue', edgecolor='black'))
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]
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for i, (title, plot_func) in enumerate(plots):
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fig, ax = plt.subplots()
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plot_func(ax)
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ax.set_title(title)
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if 'Nombre de Publications' not in title and 'Mot-Clé' not in title:
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ax.set_xlabel('Année')
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ax.set_ylabel('Nombre de Citations' if 'Citations' in title else 'Nombre de Publications')
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plt.tight_layout()
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# Sauvegarder la figure dans un objet BytesIO
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img_buffer = BytesIO()
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fig.savefig(img_buffer, format='png')
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img_buffer.seek(0)
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c.drawImage(ImageReader(img_buffer), 50, height - 320 - 150 * (i + 1), width=500, height=120)
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c.save()
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buffer.seek(0)
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return buffer
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# Exemple d'utilisation de la fonction dans Streamlit
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if st.sidebar.button("Générer le Rapport PDF"):
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years = [2020, 2021, 2022]
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keywords = ["NLP", "Artificial Intelligence"]
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author = "John Doe"
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# Simuler le chargement des données
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data = pd.read_csv('scopus_data_all_cleaned.csv')
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filtered_data = data[(data['Year'].isin(years)) & (data['Keyword'].isin(keywords))]
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buffer = generate_pdf(data, filtered_data, years, keywords, author)
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st.sidebar.success("Rapport PDF généré avec succès !")
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st.sidebar.download_button(
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label="Télécharger le PDF",
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data=buffer,
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file_name="rapport_publications_scientifiques.pdf",
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mime="application/pdf"
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)
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# Chargement des données
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data = pd.read_csv('scopus_data_all_cleaned.csv')
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# Titre et description
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st.title("Analyse des Publications Scientifiques avec l'API Scopus")
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st.markdown("""
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Ce tableau de bord vous permet d'analyser les publications scientifiques récupérées depuis l'API Scopus.
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Utilisez les filtres pour explorer les données et visualiser différentes statistiques.
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""")
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# Menu de navigation
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menu = ["Statistiques Générales", "Visualisations"]
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choice = st.sidebar.selectbox("Menu", menu)
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# Filtre pour les années, mots-clés et auteurs
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years = st.sidebar.multiselect('Sélectionnez les années', options=data['Year'].unique(), default=data['Year'].unique())
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keywords = st.sidebar.multiselect('Sélectionnez les mots-clés', options=data['Keyword'].unique(), default=data['Keyword'].unique())
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author = st.sidebar.text_input('Rechercher par auteur')
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# Filtrage des données
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filtered_data = data[(data['Year'].isin(years)) & (data['Keyword'].isin(keywords))]
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if author:
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filtered_data = filtered_data[filtered_data['Authors'].str.contains(author, case=False, na=False)]
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if choice == "Statistiques Générales":
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st.subheader("Données Filtrées")
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st.write(filtered_data)
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total_publications = len(filtered_data)
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total_citations = filtered_data['Citation Count'].sum()
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avg_citations_per_publication = filtered_data['Citation Count'].mean()
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top_cited_publication = filtered_data.loc[filtered_data['Citation Count'].idxmax()]
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st.subheader("Statistiques")
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st.write(f"**Nombre total de publications :** {total_publications}")
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st.write(f"**Nombre total de citations :** {total_citations}")
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st.write(f"**Citations moyennes par publication :** {avg_citations_per_publication:.2f}")
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st.write("**Publication avec le plus de citations :**")
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st.write(top_cited_publication)
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if st.button("Télécharger le rapport en PDF"):
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buffer = generate_pdf(data, filtered_data, years, keywords, author)
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st.download_button(
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label="Télécharger le PDF",
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data=buffer,
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file_name="rapport_publications_scientifiques.pdf",
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mime="application/pdf"
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)
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else:
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st.subheader("Visualisations")
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fig, ax = plt.subplots()
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ax.hist(filtered_data['Citation Count'], bins=20, color='skyblue', edgecolor='black')
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ax.set_title("Distribution des Citations par Publication")
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ax.set_xlabel("Nombre de Citations")
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ax.set_ylabel("Nombre de Publications")
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st.pyplot(fig)
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citations_per_year = filtered_data.groupby('Year')['Citation Count'].sum().reset_index()
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fig, ax = plt.subplots()
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ax.plot(citations_per_year['Year'], citations_per_year['Citation Count'], marker='o', color='skyblue')
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ax.set_title("Citations par Année")
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ax.set_xlabel("Année")
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ax.set_ylabel("Nombre de Citations")
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st.pyplot(fig)
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if author:
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publications_per_author = filtered_data['Authors'].value_counts().reset_index()
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publications_per_author.columns = ['Auteur', 'Nombre de Publications']
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fig, ax = plt.subplots()
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ax.bar(publications_per_author['Auteur'], publications_per_author['Nombre de Publications'], color='skyblue', edgecolor='black')
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ax.set_title("Répartition des Publications par Auteur")
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ax.set_xlabel("Auteur")
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ax.set_ylabel("Nombre de Publications")
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ax.tick_params(axis='x', rotation=90)
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st.pyplot(fig)
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fig, ax = plt.subplots()
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ax.scatter(filtered_data['Year'], filtered_data['Citation Count'], color='skyblue', edgecolor='black')
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ax.set_title("Corrélation entre le Nombre de Citations et les Années de Publication")
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ax.set_xlabel("Année")
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ax.set_ylabel("Nombre de Citations")
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st.pyplot(fig)
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publications_per_keyword = filtered_data['Keyword'].value_counts().reset_index()
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publications_per_keyword.columns = ['Keyword', 'Nombre de Publications']
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fig, ax = plt.subplots()
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ax.pie(publications_per_keyword['Nombre de Publications'], labels=publications_per_keyword['Keyword'], autopct='%1.1f%%', colors=plt.cm.Paired(range(len(publications_per_keyword))))
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ax.set_title("Nombre de Publications par Mot-Clé")
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st.pyplot(fig)
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publications_per_year = filtered_data.groupby('Year').size().reset_index(name='Nombre de Publications')
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fig, ax = plt.subplots()
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ax.plot(publications_per_year['Year'], publications_per_year['Nombre de Publications'], marker='o', color='skyblue')
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ax.set_title("Nombre de Publications par Année")
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ax.set_xlabel("Année")
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ax.set_ylabel("Nombre de Publications")
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st.pyplot(fig)
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top_authors = filtered_data.groupby('Authors')['Citation Count'].sum().reset_index().sort_values(by='Citation Count', ascending=False).head(10)
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fig, ax = plt.subplots()
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ax.bar(top_authors['Authors'], top_authors['Citation Count'], color='skyblue', edgecolor='black')
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ax.set_title("Auteurs les Plus Cités")
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ax.set_xlabel("Auteur")
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ax.set_ylabel("Nombre de Citations")
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ax.tick_params(axis='x', rotation=90)
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st.pyplot(fig)
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top_keywords = filtered_data['Keyword'].value_counts().reset_index().head(10)
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top_keywords.columns = ['Keyword', 'Nombre de Publications']
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fig, ax = plt.subplots()
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ax.bar(top_keywords['Keyword'], top_keywords['Nombre de Publications'], color='skyblue', edgecolor='black')
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ax.set_title("Sujets les Plus Publiés")
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ax.set_xlabel("Mot-Clé")
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ax.set_ylabel("Nombre de Publications")
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ax.tick_params(axis='x', rotation=90)
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st.pyplot(fig)
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pfm_Python_finale.ipynb
ADDED
The diff for this file is too large to render.
See raw diff
|
|
scopus_data_All.csv
ADDED
The diff for this file is too large to render.
See raw diff
|
|
scopus_data_all_cleaned.csv
ADDED
The diff for this file is too large to render.
See raw diff
|
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