Update pages/4 Sunburst.py

pages/4 Sunburst.py

@@ -1,8 +1,19 @@
-#===import module===
 import streamlit as st
 import pandas as pd
-import plotly.express as px
+from sklearn.feature_extraction.text import CountVectorizer
+from nltk.tokenize import word_tokenize
+from nltk.corpus import stopwords
+import nltk
+import spacy
+from burst_detection import burst_detection, enumerate_bursts, burst_weights
+import matplotlib.pyplot as plt
+import os
+import io
+import math
 import numpy as np
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+import plotly.io as pio
 import sys
 
 #===config===
@@ -31,114 +42,384 @@ with st.popover("🔗 Menu"):
     st.page_link("pages/4 Sunburst.py", label="Sunburst", icon="4️⃣")
     st.page_link("pages/5 Burst Detection.py", label="Burst Detection", icon="5️⃣")
     st.page_link("pages/6 Keywords Stem.py", label="Keywords Stem", icon="6️⃣")
-    
-st.header("Sunburst Visualization", anchor=False)
+
+st.header("Burst Detection", anchor=False)
 st.subheader('Put your file here...', anchor=False)
 
 #===clear cache===
 def reset_all():
     st.cache_data.clear()
 
-#===check type===
+# Initialize NLP model
+nlp = spacy.load("en_core_web_md")
+
 @st.cache_data(ttl=3600)
-def get_ext(extype):
+def upload(extype):
+    df = pd.read_csv(uploaded_file)
+    #lens.org
+    if 'Publication Year' in df.columns:
+               df.rename(columns={'Publication Year': 'Year', 'Citing Works Count': 'Cited by',
+                                     'Publication Type': 'Document Type', 'Source Title': 'Source title'}, inplace=True)
+    return df
+
+@st.cache_data(ttl=3600)
+def get_ext(uploaded_file):
     extype = uploaded_file.name
     return extype
 
 @st.cache_data(ttl=3600)
-def upload(extype):
-    papers = pd.read_csv(uploaded_file)
-    #lens.org
-    if 'Publication Year' in papers.columns:
-            papers.rename(columns={'Publication Year': 'Year', 'Citing Works Count': 'Cited by',
-                                    'Publication Type': 'Document Type', 'Source Title': 'Source title'}, inplace=True)
-    return papers
+def get_minmax(df):
+    MIN = int(df['Year'].min())
+    MAX = int(df['Year'].max())
+    GAP = MAX - MIN
+    return MIN, MAX, GAP
 
 @st.cache_data(ttl=3600)
 def conv_txt(extype):
     col_dict = {'TI': 'Title',
             'SO': 'Source title',
             'DT': 'Document Type',
-            'DE': 'Author Keywords',
-            'ID': 'Keywords Plus',
             'AB': 'Abstract',
-            'TC': 'Cited by',
-            'PY': 'Year',}
-    papers = pd.read_csv(uploaded_file, sep='\t', lineterminator='\r')
-    papers.rename(columns=col_dict, inplace=True)
-    return papers
+            'PY': 'Year'}
+    df = pd.read_csv(uploaded_file, sep='\t', lineterminator='\r')
+    df.rename(columns=col_dict, inplace=True)
+    return df
+
+# Helper Functions
+@st.cache_data(ttl=3600)
+def get_column_name(df, possible_names):
+    """Find and return existing column names from a list of possible names."""
+    for name in possible_names:
+        if name in df.columns:
+            return name
+    raise ValueError(f"None of the possible names {possible_names} found in DataFrame columns.")
+
+@st.cache_data(ttl=3600)
+def preprocess_text(text):
+    """Lemmatize and remove stopwords from text."""
+    return ' '.join([token.lemma_.lower() for token in nlp(text) if token.is_alpha and not token.is_stop])
+
+@st.cache_data(ttl=3600)
+def load_data(uploaded_file):
+    """Load data from the uploaded file."""
+    extype = get_ext(uploaded_file)
+    if extype.endswith('.csv'):
+         df = upload(extype) 
+    elif extype.endswith('.txt'):
+         df = conv_txt(extype)
+
+    df['Year'] = pd.to_numeric(df['Year'], errors='coerce')
+    df = df.dropna(subset=['Year'])
+    df['Year'] = df['Year'].astype(int)
+        
+    if 'Title' in df.columns and 'Abstract' in df.columns:
+        coldf = ['Abstract', 'Title']
+    elif 'Title' in df.columns:
+        coldf = ['Title']
+    elif 'Abstract' in df.columns:
+        coldf = ['Abstract']
+    else:
+        coldf = sorted(df.select_dtypes(include=['object']).columns.tolist())
+
+    MIN, MAX, GAP = get_minmax(df)
 
-#===Read data===
+    return df, coldf, MIN, MAX, GAP
+
+@st.cache_data(ttl=3600)
+def clean_data(df):
+
+    years = list(range(YEAR[0],YEAR[1]+1))
+    df = df.loc[df['Year'].isin(years)]
+    
+    # Preprocess text
+    df['processed'] = df.apply(lambda row: preprocess_text(f"{row.get(col_name, '')}"), axis=1)
+    
+    # Vectorize processed text
+    vectorizer = CountVectorizer(lowercase=False, tokenizer=lambda x: x.split())
+    X = vectorizer.fit_transform(df['processed'].tolist())
+    
+    # Create DataFrame from the Document-Term Matrix (DTM)
+    dtm = pd.DataFrame(X.toarray(), columns=vectorizer.get_feature_names_out(), index=df['Year'].values)
+    yearly_term_frequency = dtm.groupby(dtm.index).sum()
+
+    # User inputs for top words analysis and exclusions
+    excluded_words = [word.strip() for word in excluded_words_input.split(',')]
+    
+    # Identify top words, excluding specified words
+    filtered_words = [word for word in yearly_term_frequency.columns if word not in excluded_words]
+    top_words = yearly_term_frequency[filtered_words].sum().nlargest(top_n).index.tolist()
+    
+    return yearly_term_frequency, top_words
+
+@st.cache_data(ttl=3600)
+def apply_burst_detection(top_words, data):
+    all_bursts_list = []
+
+    start_year = int(data.index.min())
+    end_year = int(data.index.max())
+    all_years = range(start_year, end_year + 1)
+    
+    continuous_years = pd.Series(index=all_years, data=0)  # Start with a series of zeros for all years
+
+    years = continuous_years.index.tolist()
+    
+    all_freq_data = pd.DataFrame(index=years)
+    
+    for i, word in enumerate(top_words, start=1):
+        # Update with actual counts where available
+        word_counts = data[word].reindex(continuous_years.index, fill_value=0)
+        
+        # Convert years and counts to lists for burst detection
+        r = continuous_years.index.tolist()  # List of all years
+        r = np.array(r, dtype=int)
+        d = word_counts.values.tolist()  # non-zero counts
+        d = np.array(d, dtype=float)
+        y = r.copy()
+     
+        if len(r) > 0 and len(d) > 0:
+            n = len(r)
+            q, d, r, p = burst_detection(d, r, n, s=2.0, gamma=1.0, smooth_win=1)
+            bursts = enumerate_bursts(q, word)
+            bursts = burst_weights(bursts, r, d, p)
+            all_bursts_list.append(bursts)
+    
+            freq_data = yearly_term_frequency[word].reindex(years, fill_value=0)
+            all_freq_data[word] = freq_data
+
+    all_bursts = pd.concat(all_bursts_list, ignore_index=True)
+
+    num_unique_labels = len(all_bursts['label'].unique())
+
+    num_rows = math.ceil(top_n / 2)
+
+    if running_total == "Running total":
+        all_freq_data = all_freq_data.cumsum()
+                        
+    return all_bursts, all_freq_data, num_unique_labels, num_rows
+
+@st.cache_data(ttl=3600)
+def convert_df(df):
+    return df.to_csv().encode("utf-8")
+
+@st.cache_data(ttl=3600)
+def scattervis(bursts, freq_data):
+    freq_data.reset_index(inplace=True)
+    freq_data.rename(columns={"index": "Year"}, inplace=True)
+                        
+    freq_data_melted = freq_data.melt(id_vars=["Year"], var_name="Category", value_name="Value")
+    freq_data_melted = freq_data_melted[freq_data_melted["Value"] > 0]
+    wordlist = freq_data_melted["Category"].unique()
+    
+    years = freq_data["Year"].tolist()
+    bursts["begin"] = bursts["begin"].apply(lambda x: years[min(x, len(years) - 1)] if x < len(years) else None)
+    bursts["end"] = bursts["end"].apply(lambda x: years[min(x, len(years) - 1)] if x < len(years) else None)
+    burst_points = []
+    
+    for _, row in bursts.iterrows():
+        for year in range(row["begin"], row["end"] + 1):
+            burst_points.append((year, row["label"], row["weight"]))
+                    
+    burst_points_df = pd.DataFrame(burst_points, columns=["Year", "Category", "Weight"])
+                    
+    fig = go.Figure()
+    
+    # scatter trace for burst points
+    fig.add_trace(go.Scatter(
+        x=burst_points_df["Year"],
+        y=burst_points_df["Category"],
+        mode='markers',
+        marker=dict(
+            symbol='square',
+            size=40,  
+            color='red',
+            opacity=0.5),
+        hoverinfo='text',
+        text=burst_points_df["Weight"],
+        showlegend=False
+    ))
+                    
+    # scatter trace for freq_data
+    fig.add_trace(go.Scatter(
+        x=freq_data_melted["Year"],
+        y=freq_data_melted["Category"],
+        mode='markers+text',
+        marker=dict(
+            symbol='square',
+            size=30,
+            color=freq_data_melted["Value"],
+            colorscale='Blues',
+            showscale=False),
+        text=freq_data_melted["Value"],
+        textposition="middle center",
+        textfont=dict(
+            size=16,
+            color=['white' if value > freq_data_melted["Value"].max()/2 else 'black' for value in freq_data_melted["Value"]])
+    ))
+    
+    min_year = min(years)
+    max_year = max(years)
+                    
+    fig.update_layout(
+        xaxis=dict(tickmode='linear', dtick=1, range=[(min_year-1), (max_year+1)], tickfont = dict(size=16), automargin=True, showgrid=False, zeroline=False),
+        yaxis=dict(tickvals=wordlist, ticktext=wordlist, tickmode='array', tickfont = dict(size=16), automargin=True, showgrid=False, zeroline=False),
+        plot_bgcolor='white',  
+        paper_bgcolor='white',  
+        showlegend=False,
+        margin=dict(l=1, r=1, t=1, b=1),
+        height=top_n*50+2,
+        width=(max_year-min_year)*52+100,
+        autosize=False
+    )
+                    
+    fig.write_image("scatter_plot.png")
+    st.image("scatter_plot.png")
+    pio.write_image(fig, 'result.png', scale=4)  
+
+@st.cache_data(ttl=3600)
+def linegraph(bursts, freq_data):
+    fig = make_subplots(rows=num_rows, cols=2, subplot_titles=freq_data.columns[:top_n])
+    
+    row, col = 1, 1
+    for i, column in enumerate(freq_data.columns[:top_n]):
+        fig.add_trace(go.Scatter(
+            x=freq_data.index, y=freq_data[column], mode='lines+markers+text', name=column,
+            line_shape='linear',
+            hoverinfo='text',
+            hovertext=[f"Year: {index}<br>Frequency: {freq}" for index, freq in zip(freq_data.index, freq_data[column])],
+            text=freq_data[column],
+            textposition='top center'
+        ), row=row, col=col)
+                
+        # Add area charts
+        for _, row_data in bursts[bursts['label'] == column].iterrows():
+            x_values = freq_data.index[row_data['begin']:row_data['end']+1]
+            y_values = freq_data[column][row_data['begin']:row_data['end']+1]
+                        
+            #middle_y = sum(y_values) / len(y_values)
+            y_post = min(freq_data[column]) + 1 if running_total == "Running total" else sum(y_values) / len(y_values)
+            x_offset = 0.1
+                        
+            # Add area chart
+            fig.add_trace(go.Scatter(
+                x=x_values,
+                y=y_values,
+                fill='tozeroy', mode='lines', fillcolor='rgba(0,100,80,0.2)',
+            ), row=row, col=col)
+    
+            align_value = "left" if running_total == "Running total" else "center"
+            valign_value = "bottom" if running_total == "Running total" else "middle"
+                                            
+            # Add annotation for weight at the bottom
+            fig.add_annotation(
+                x=x_values[0] + x_offset,
+                y=y_post,
+                text=f"Weight: {row_data['weight']:.2f}",
+                showarrow=False,
+                font=dict(
+                    color="black",
+                    size=12),
+                align=align_value,
+                valign=valign_value,
+                textangle=270,
+                row=row, col=col
+                )
+                
+        col += 1
+        if col > 2:
+            col = 1
+            row += 1
+                
+    fig.update_layout(
+        showlegend=False,
+        margin=dict(l=20, r=20, t=100, b=20),
+        height=num_rows * 500,
+        width=1500
+    )
+                
+    fig.write_image("line_graph.png")
+    st.image("line_graph.png")
+    pio.write_image(fig, 'result.png', scale=4)
+
+@st.cache_data(ttl=3600)
+def download_result(freq_data, bursts):
+    csv1 = convert_df(freq_data)
+    csv2 = convert_df(bursts)
+    return csv1, csv2
+      
 uploaded_file = st.file_uploader('', type=['csv', 'txt'], on_change=reset_all)
 
 if uploaded_file is not None:
     try:
-        extype = get_ext(uploaded_file)
-        if extype.endswith('.csv'):
-             papers = upload(extype) 
-       
-        elif extype.endswith('.txt'):
-             papers = conv_txt(extype)
-        
-        @st.cache_data(ttl=3600)
-        def get_minmax(extype):
-            extype = extype
-            MIN = int(papers['Year'].min())
-            MAX = int(papers['Year'].max())
-            GAP = MAX - MIN
-            return papers, MIN, MAX, GAP
-        
-        tab1, tab2 = st.tabs(["📈 Generate visualization", "📓 Recommended Reading"])
+        c1, c2, c3 = st.columns([3,3.5,3.5])
+        top_n = c1.number_input("Number of top words to analyze", min_value=5, value=10, step=1, on_change=reset_all)
+        viz_selected = c2.selectbox("Option for visualization",
+            ("Line graph", "Scatter plot"), on_change=reset_all)
+        running_total = c3.selectbox("Option for counting words",
+            ("Running total", "By occurrences each year"), on_change=reset_all)
+
+        d1, d2 = st.columns([3,7])
+        df, coldf, MIN, MAX, GAP = load_data(uploaded_file)
+        col_name = d1.selectbox("Select column to analyze",
+            (coldf), on_change=reset_all)
+        excluded_words_input = d2.text_input("Words to exclude (comma-separated)", on_change=reset_all)
+
+        if (GAP != 0):
+            YEAR = st.slider('Year', min_value=MIN, max_value=MAX, value=(MIN, MAX), on_change=reset_all)
+        else:
+            e1.write('You only have data in ', (MAX))
+            sys.exit(1)
+      
+        yearly_term_frequency, top_words = clean_data(df) 
         
-        with tab1:    
-            #===sunburst===
-            try:
-                papers, MIN, MAX, GAP = get_minmax(extype)
-            except KeyError:
-                st.error('Error: Please check again your columns.')
-                sys.exit(1)
-            
-            if (GAP != 0):
-                YEAR = st.slider('Year', min_value=MIN, max_value=MAX, value=(MIN, MAX), on_change=reset_all)
-            else:
-                st.write('You only have data in ', (MAX))
-                YEAR = (MIN, MAX)
-            
-            @st.cache_data(ttl=3600)
-            def listyear(extype):
-                global papers
-                years = list(range(YEAR[0],YEAR[1]+1))
-                papers = papers.loc[papers['Year'].isin(years)]
-                return years, papers
-            
-            @st.cache_data(ttl=3600)
-            def vis_sunbrust(extype):
-                papers['Cited by'] = papers['Cited by'].fillna(0)
-                vis = pd.DataFrame()
-                vis[['doctype','source','citby','year']] = papers[['Document Type','Source title','Cited by','Year']]
-                viz=vis.groupby(['doctype', 'source', 'year'])['citby'].agg(['sum','count']).reset_index()  
-                viz.rename(columns={'sum': 'cited by', 'count': 'total docs'}, inplace=True)
-                                
-                fig = px.sunburst(viz, path=['doctype', 'source', 'year'], values='total docs',
-                              color='cited by', 
-                              color_continuous_scale='RdBu',
-                              color_continuous_midpoint=np.average(viz['cited by'], weights=viz['total docs']))
-                fig.update_layout(height=800, width=1200)
-                return fig
-            
-            years, papers = listyear(extype)
+        bursts, freq_data, num_unique_labels, num_rows = apply_burst_detection(top_words, yearly_term_frequency)
+
+        tab1, tab2, tab3 = st.tabs(["📈 Generate visualization", "📃 Reference", "📓 Recommended Reading"])
+
+        with tab1:        
+            if bursts.empty:
+                st.warning('We cannot detect any bursts', icon='⚠️')
     
-            if {'Document Type','Source title','Cited by','Year'}.issubset(papers.columns):
-                fig = vis_sunbrust(extype)
-                st.plotly_chart(fig, height=800, width=1200) #use_container_width=True)
-               
             else:
-                st.error('We require these columns: Document Type, Source title, Cited by, Year', icon="🚨")
-        
-        with tab2:
-            st.markdown('**numpy.average — NumPy v1.24 Manual. (n.d.). Numpy.Average — NumPy v1.24 Manual.** https://numpy.org/doc/stable/reference/generated/numpy.average.html')
-            st.markdown('**Sunburst. (n.d.). Sunburst Charts in Python.** https://plotly.com/python/sunburst-charts/')
+                if num_unique_labels == top_n:
+                    st.info(f'We detect a burst on {num_unique_labels} word(s)', icon="ℹ️")
+                elif num_unique_labels < top_n:
+                    st.info(f'We only detect a burst on {num_unique_labels} word(s), which is {top_n - num_unique_labels} fewer than the top word(s)', icon="ℹ️")
+
+                if viz_selected == "Line graph": 
+                    linegraph(bursts, freq_data)
+                    
+                elif viz_selected =="Scatter plot":
+                    scattervis(bursts, freq_data)
+                
+                csv1, csv2 = download_result(freq_data, bursts)
+                e1, e2, e3 = st.columns(3)
+                with open('result.png', "rb") as file:
+                    btn = e1.download_button(
+                        label="📊 Download high resolution image",
+                        data=file,
+                        file_name="burst.png",
+                        mime="image/png")
+                    
+                e2.download_button(
+                    "👉 Press to download list of top words",
+                    csv1,
+                    "top-keywords.csv",
+                    "text/csv")
     
+                e3.download_button(
+                    "👉 Press to download the list of detected bursts",
+                    csv2,
+                    "burst.csv",
+                    "text/csv")
+ 
+        with tab2:
+            st.markdown('**Kleinberg, J. (2002). Bursty and hierarchical structure in streams. Knowledge Discovery and Data Mining.** https://doi.org/10.1145/775047.775061')
+
+        with tab3:
+            st.markdown('**Li, M., Zheng, Z., & Yi, Q. (2024). The landscape of hot topics and research frontiers in Kawasaki disease: scientometric analysis. Heliyon, 10(8), e29680–e29680.** https://doi.org/10.1016/j.heliyon.2024.e29680')
+            st.markdown('**Domicián Máté, Ni Made Estiyanti and Novotny, A. (2024) ‘How to support innovative small firms? Bibliometric analysis and visualization of start-up incubation’, Journal of Innovation and Entrepreneurship, 13(1).** https://doi.org/10.1186/s13731-024-00361-z')
+            st.markdown('**Lamba, M., Madhusudhan, M. (2022). Burst Detection. In: Text Mining for Information Professionals. Springer, Cham.** https://doi.org/10.1007/978-3-030-85085-2_6')
+            
     except:
         st.error("Please ensure that your file is correct. Please contact us if you find that this is an error.", icon="🚨")
         st.stop()