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