Create charts_advanced.py

charts_advanced.py

@@ -0,0 +1,343 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+from collections import Counter
+import matplotlib.ticker as ticker
+
+def category_chart(file_path):
+    plt.close('all')
+    # Define expert to specialty mapping
+    expert_specialties = {
+        "mireille": "Security Trust",
+        "khawla": "Network Security",
+        "guillaume": "Distributed Networks",
+        "vincent": "USIM Management",
+        "pierre": "Eco-Design",
+        "ly-thanh": "Trend Analysis",
+        "nicolas": "Satellite Networks",
+        "dorin": "Emergency Communication"
+    }
+
+    # Load the Excel file
+    data = pd.read_excel(file_path)
+
+    # Assuming experts are listed in a column named 'Experts'
+    # This part might need to be adjusted based on the actual structure of your Excel file
+    experts = data['Expert'].dropna()
+
+    # Map experts to their specialties
+    specialties = experts.apply(lambda expert: expert_specialties.get(expert.strip(), "Other"))
+
+    # Count occurrences
+    specialty_counts = specialties.value_counts()
+
+    # Convert to DataFrame for plotting
+    specialty_counts_df = specialty_counts.reset_index()
+    specialty_counts_df.columns = ['Specialty', 'Count']
+
+    # Plotting
+    plt.style.use('dark_background')
+    fig, ax = plt.subplots(figsize=(14, 14))
+    ax.set_facecolor('#222c52')
+    fig.patch.set_facecolor('#222c52')
+
+    # Alternating colors for the bars
+    colors = ['#08F7FE' if i % 2 == 0 else '#FE53BB' for i in range(len(specialty_counts_df))]
+    specialty_counts_df.plot(kind='bar', x='Specialty', y='Count', ax=ax, color=colors, edgecolor=colors, alpha=0.5, linewidth=5, legend=None)
+
+    # Set chart details
+    ax.xaxis.label.set_color('white')
+    ax.yaxis.label.set_color('white')
+    ax.tick_params(axis='x', colors='white', labelsize=12, direction='out', length=6, width=2, rotation=42)
+    ax.tick_params(axis='y', colors='white', labelsize=12, direction='out', length=6, width=2)
+    ax.set_title('Most Used Expert Specialties', color='white', fontsize=16)
+    ax.set_xlabel('Specialty', fontsize=14)
+    ax.set_ylabel('Count', fontsize=14)
+    ax.grid(True, which='both', axis='y', color='gray', linestyle='-', linewidth=0.5, alpha=0.5)
+    ax.set_axisbelow(True)
+
+    for spine in ax.spines.values():
+        spine.set_color('white')
+        spine.set_linewidth(2)
+    ax.spines['right'].set_visible(False)
+    ax.spines['top'].set_visible(False)
+
+    return fig
+
+
+
+
+def status_chart(file_path):
+    # Load the Excel file
+    plt.close('all')
+    data = pd.read_excel(file_path)
+
+    # Calculate the frequency of each status
+    status_counts = data['Status'].value_counts()
+
+    # Define colors with 50% opacity
+    colors = ['#08F7FE80', '#FE53BB80',
+              '#fff236de', '#90ff00bf']  # '80' for 50% opacity
+
+    # Plotting
+    fig, ax = plt.subplots()
+    fig.patch.set_facecolor('#222c52')  # Set the background color of the figure
+    ax.set_facecolor('#222c52')  # Set the background color of the axes
+    wedges, texts, autotexts = ax.pie(status_counts, autopct='%1.1f%%', startangle=90, colors=colors,
+                                      wedgeprops=dict(edgecolor='white', linewidth=1.5))
+
+    # Set legend
+    ax.legend(wedges, status_counts.index, title="Document Status", loc="center left", bbox_to_anchor=(1, 0, 0.5, 1))
+
+    ax.set_ylabel('')  # Remove the y-label
+    ax.set_title('Document Status Distribution', color='white')
+
+    plt.setp(autotexts, size=8, weight="bold", color="white")
+
+    return fig
+
+
+
+def plot_glowing_line_with_dots_enhanced(ax, x, y, color, label, glow_size=10, base_linewidth=3, markersize=8):
+    for i in range(1, glow_size + 1):
+        alpha_value = (1.0 / glow_size) * (i / (glow_size / 2))
+        if alpha_value > 1.0:
+            alpha_value = 1.0
+        linewidth = base_linewidth * i * 0.5
+        ax.plot(x, y, color=color, linewidth=linewidth, alpha=alpha_value * 0.1)
+    ax.plot(x, y, color=color, linewidth=base_linewidth, marker='o', linestyle='-', label=label, markersize=markersize)
+
+def company_document_type(file_path, company_names):
+    plt.close('all')
+    # Convert company_names to a list if it's a string
+    if isinstance(company_names, str):
+        company_names = [name.strip() for name in company_names.split(',')]  # Ensure it's a list even for single company name
+
+    df = pd.read_excel(file_path)
+    plt.style.use('dark_background')
+    fig, ax = plt.subplots(figsize=(14, 8))
+    ax.set_facecolor('#222c52')
+    fig.patch.set_facecolor('#222c52')
+
+    colors = ['#08F7FE', '#FE53BB', '#fff236']  # Assign more colors for more companies
+
+    max_count = 0
+    for index, company_name in enumerate(company_names):
+        df_company = df[df['Source'].str.contains(company_name, case=False, na=False)]
+        document_counts = df_company['Type'].value_counts()
+        all_document_types = df['Type'].unique()
+        document_counts = document_counts.reindex(all_document_types, fill_value=0)
+
+        x_data = document_counts.index
+        y_data = document_counts.values
+        ax.fill_between(x_data, y_data, -0.2, color=colors[index % len(colors)], alpha=0.1)
+        plot_glowing_line_with_dots_enhanced(ax, x_data, y_data, colors[index % len(colors)], company_name, base_linewidth=4)
+
+        if max_count < max(y_data):
+            max_count = max(y_data)
+
+    ax.set_xticks(range(len(all_document_types)))
+    ax.set_xticklabels(all_document_types, rotation=45, fontsize=12, fontweight='bold')
+    ax.yaxis.set_major_locator(ticker.MaxNLocator(integer=True))
+    ax.set_ylabel('Count', color='white')
+    ax.set_title('Document Types Contributed by Companies')
+    ax.grid(True, which='both', axis='both', color='gray', linestyle='-', linewidth=0.5, alpha=0.5)
+    ax.set_axisbelow(True)
+
+    plt.ylim(-0.2, max_count + 1)
+
+    for spine in ax.spines.values():
+        spine.set_color('white')
+        spine.set_linewidth(2)
+
+    ax.spines['right'].set_visible(False)
+    ax.spines['top'].set_visible(False)
+    ax.spines['left'].set_position(('data', 0))
+    plt.legend(facecolor='#222c52', edgecolor='white', fontsize=12)
+
+    return fig
+
+
+
+def chart_by_expert(file_path, expert_name):
+    plt.close('all')
+    # Load the Excel file
+    data = pd.read_excel(file_path)
+    
+    parts = expert_name.split('/')
+
+# The name would be the second part, trim spaces
+    name = parts[1].strip()
+    # Filter data for the specified expert
+    filtered_data = data[data['Expert'] == name.lower()]
+
+    # Define merge entities mapping
+    merge_entities = {
+        "Nokia Shanghai Bell": "Nokia",
+        "Qualcomm Korea": "Qualcomm",
+        "Qualcomm Incorporated": "Qualcomm",
+        "Huawei Technologies R&D UK": "Huawei",
+        "Hughes Network Systems": "Hughes",
+        "HUGHES Network Systems": "Hughes",
+        "Hughes Network systems": "Hughes",
+        "HUGHES Network Systems Ltd": "Hughes",
+        "KT Corp.": "KT Corporation",
+        "LG Electronics Inc.": "LG Electronics",
+        "LG Uplus": "LG Electronics",
+        "OPPO (chongqing) Intelligence": "OPPO",
+        "Samsung Electronics GmbH": "Samsung",
+        "China Mobile International Ltd": "China Mobile",
+        "NOVAMINT": "Novamint",
+        "Eutelsat": "Eutelsat Group",
+        "Inmarsat Viasat": "Inmarsat",
+        "China Telecommunications": "China Telecom",
+        "SES S.A.": "SES",
+        "Ericsson GmbH": "Ericsson",
+        "JSAT": "SKY Perfect JSAT",
+        "NEC Europe Ltd": "NEC",
+        "Fraunhofer IIS": "Fraunhofer",
+        "Hugues Network Systems": "Hughes"
+    }
+
+    # Normalize company names within each cell
+    def normalize_companies(company_list, merge_entities):
+        normalized = set()  # Use a set to avoid duplicates within the same cell
+        for company in company_list:
+            normalized_name = merge_entities.get(company.strip(), company.strip())
+            normalized.add(normalized_name)
+        return list(normalized)
+
+    # Prepare the filtered data
+    sources = filtered_data['Source'].dropna()
+    split_sources = sources.apply(lambda x: normalize_companies(x.split(', '), merge_entities))
+
+    # Flatten the list of lists while applying the merge rules
+    all_sources = [company for sublist in split_sources for company in sublist]
+
+    # Count occurrences
+    source_counts = Counter(all_sources)
+    top_10_sources = source_counts.most_common(10)
+
+    # Convert to DataFrame for plotting
+    top_10_df = pd.DataFrame(top_10_sources, columns=['Company', 'Count'])
+
+    # Plotting
+    plt.style.use('dark_background')
+    fig, ax = plt.subplots(figsize=(14, 11))
+    ax.set_facecolor('#222c52')
+    fig.patch.set_facecolor('#222c52')
+
+    # Alternating colors for the bars
+    colors = ['#08F7FE' if i % 2 == 0 else '#FE53BB' for i in range(len(top_10_df))]
+    top_10_df.plot(kind='bar', x='Company', y='Count', ax=ax, color=colors, edgecolor=colors, alpha=0.5, linewidth=5)
+
+    # Set chart details
+    ax.xaxis.label.set_color('white')
+    ax.yaxis.label.set_color('white')
+    ax.tick_params(axis='x', colors='white', labelsize=12, direction='out', length=6, width=2, rotation=45)
+    ax.tick_params(axis='y', colors='white', labelsize=12, direction='out', length=6, width=2)
+    ax.set_title(f"Top 10 Cotributors for Expert '{expert_name}'", color='white', fontsize=16)
+    ax.set_xlabel('Company', fontsize=14)
+    ax.set_ylabel('Count', fontsize=14)
+    ax.yaxis.set_major_locator(ticker.MaxNLocator(integer=True))
+    ax.grid(True, which='both', axis='y', color='gray', linestyle='-', linewidth=0.5, alpha=0.5)
+    ax.set_axisbelow(True)
+
+    for spine in ax.spines.values():
+        spine.set_color('white')
+        spine.set_linewidth(2)
+    ax.spines['right'].set_visible(False)
+    ax.spines['top'].set_visible(False)
+
+    return fig
+
+
+
+# @title Top 10 des entreprises en termes de publications
+
+
+
+def generate_company_chart(file_path):
+    # plt.close('all')
+    # Define merge entities mapping
+    merge_entities = {
+        "Nokia Shanghai Bell": "Nokia",
+        "Qualcomm Korea": "Qualcomm",
+        "Qualcomm Incorporated": "Qualcomm",
+        "Huawei Technologies R&D UK": "Huawei",
+        "Hughes Network Systems": "Hughes",
+        "HUGHES Network Systems": "Hughes",
+        "Hughes Network systems": "Hughes",
+        "HUGHES Network Systems Ltd": "Hughes",
+        "KT Corp.": "KT Corporation",
+        "Deutsche Telekom AG": "Deutsche Telekom",
+        "LG Electronics Inc.": "LG Electronics",
+        "LG Uplus": "LG Electronics",
+        "OPPO (chongqing) Intelligence": "OPPO",
+        "Samsung Electronics GmbH": "Samsung",
+        "China Mobile International Ltd": "China Mobile",
+        "NOVAMINT": "Novamint",
+        "Eutelsat": "Eutelsat Group",
+        "Inmarsat Viasat": "Inmarsat",
+        "China Telecommunications": "China Telecom",
+        "SES S.A.": "SES",
+        "Ericsson GmbH": "Ericsson",
+        "JSAT": "SKY Perfect JSAT",
+        "NEC Europe Ltd": "NEC",
+        "Fraunhofer IIS": "Fraunhofer",
+        "Hugues Network Systems": "Hughes"
+    }
+
+    # Function to normalize company names within each cell
+    def normalize_companies(company_list, merge_entities):
+        normalized = set()  # Use a set to avoid duplicates within the same cell
+        for company in company_list:
+            normalized_name = merge_entities.get(company.strip(), company.strip())
+            normalized.add(normalized_name)
+        return list(normalized)
+
+    # Load the Excel file
+    data = pd.read_excel(file_path)
+
+    # Prepare the data
+    sources = data['Source'].dropna()
+    split_sources = sources.apply(lambda x: normalize_companies(x.split(', '), merge_entities))
+
+    # Flatten the list of lists while applying the merge rules
+    all_sources = [company for sublist in split_sources for company in sublist]
+
+    # Count occurrences
+    source_counts = Counter(all_sources)
+    top_10_sources = source_counts.most_common(10)
+
+    # Convert to DataFrame for plotting
+    top_10_df = pd.DataFrame(top_10_sources, columns=['Company', 'Count'])
+
+    # Plotting
+    plt.style.use('dark_background')
+    fig, ax = plt.subplots(figsize=(14, 12))
+    ax.set_facecolor('#222c52')
+    fig.patch.set_facecolor('#222c52')
+
+    # Alternating colors for the bars
+    colors = ['#08F7FE' if i % 2 == 0 else '#FE53BB' for i in range(len(top_10_df))]
+    top_10_df.plot(kind='bar', x='Company', y='Count', ax=ax, color=colors, edgecolor=colors, alpha=0.5, linewidth=5, legend=None)
+
+    # Set chart details
+    ax.xaxis.label.set_color('white')
+    ax.yaxis.label.set_color('white')
+    ax.tick_params(axis='x', colors='white', labelsize=16, direction='out', length=6, width=2, rotation=37)
+    ax.tick_params(axis='y', colors='white', labelsize=12, direction='out', length=6, width=2)
+    ax.set_title('Top 10 Contributors: Ranking Company Contributions', color='white', fontsize=16)
+    ax.set_xlabel('Company', fontsize=14)
+    ax.set_ylabel('Count', fontsize=14)
+    ax.grid(True, which='both', axis='y', color='gray', linestyle='-', linewidth=0.5, alpha=0.5)
+    ax.set_axisbelow(True)
+
+    for spine in ax.spines.values():
+        spine.set_color('white')
+        spine.set_linewidth(2)
+    ax.spines['right'].set_visible(False)
+    ax.spines['top'].set_visible(False)
+
+    #plt.show()
+    return fig