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| import plotly.express as px | |
| import numpy as np | |
| paper_dump = pd.read_csv('data/dump.csv', sep="\t") | |
| # Calculate total number of URLs per year and venue | |
| custom_order = ["MICCAI", "MIDL", "Nature", "arXiv"] | |
| total_titles_per_venue = paper_dump.groupby(['year', 'venue']).size().reset_index(name='total_titles') | |
| # Calculate the number of URLs with errors per year and venue | |
| total_url_per_venue = paper_dump[paper_dump["url"] != ""].groupby(['year', 'venue']).size().reset_index(name='total_urls') | |
| # Merge the DataFrames to calculate the error rate | |
| merged_df = pd.merge(total_titles_per_venue, total_url_per_venue, on=['year', 'venue'], how='left') | |
| merged_df['repo_rate'] = merged_df['total_urls'] / merged_df['total_titles'] | |
| # Plot the error rates using Plotly, with year on x-axis and color by venue | |
| fig = px.bar( | |
| merged_df, | |
| x='year', | |
| y='total_titles', | |
| color='venue', | |
| barmode='group', | |
| title=f'Number of papers per venue', | |
| labels={'error_rate': 'Success Rate', 'year': 'Year'}, | |
| category_orders={'venue': custom_order} | |
| ) | |
| fig.update_xaxes(range=[2018, 2024]) | |
| fig.show() | |
| import plotly.express as px | |
| import numpy as np | |
| # Calculate total number of URLs per year and venue | |
| total_titles_per_venue = paper_dump.groupby(['year', 'venue']).size().reset_index(name='total_titles') | |
| # Calculate the number of URLs with errors per year and venue | |
| total_url_per_venue = paper_dump[paper_dump["url"] != ""].groupby(['year', 'venue']).size().reset_index(name='total_urls') | |
| # Merge the DataFrames to calculate the error rate | |
| merged_df = pd.merge(total_titles_per_venue, total_url_per_venue, on=['year', 'venue'], how='left') | |
| merged_df['repo_rate'] = merged_df['total_urls'] / merged_df['total_titles'] | |
| # Plot the error rates using Plotly, with year on x-axis and color by venue | |
| fig = px.bar( | |
| merged_df, | |
| x='year', | |
| y='total_titles', | |
| color='venue', | |
| barmode='group', | |
| title=f'Number of papers per venue', | |
| labels={'error_rate': 'Success Rate', 'year': 'Year'}, | |
| category_orders={'venue': custom_order} | |
| ) | |
| fig.update_xaxes(range=[2018, 2024]) | |
| fig.show() | |
| # Plot the error rates using Plotly, with year on x-axis and color by venue | |
| fig = px.bar( | |
| merged_df, | |
| x='year', | |
| y='total_urls', | |
| color='venue', | |
| barmode='group', | |
| title=f'Number of papers per venue', | |
| labels={'error_rate': 'Success Rate', 'year': 'Year'}, | |
| category_orders={'venue': custom_order} | |
| ) | |
| fig.update_xaxes(range=[2018, 2024]) | |
| fig.show() | |
| # Plot the error rates using Plotly, with year on x-axis and color by venue | |
| fig = px.bar( | |
| merged_df, | |
| x='year', | |
| y='repo_rate', | |
| color='venue', | |
| barmode='group', | |
| title=f'Number of repositories per venue', | |
| labels={'error_rate': 'Success Rate', 'year': 'Year'}, | |
| category_orders={'venue': custom_order} | |
| ) | |
| fig.update_xaxes(range=[2018, 2024]) | |
| fig.update_yaxes(range=[0, 1]) | |
| fig.show() |