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| # Copyright 2023 by Jan Philip Wahle, https://jpwahle.com/ | |
| # All rights reserved. | |
| import os | |
| import numpy as np | |
| import pandas as pd | |
| import seaborn as sns | |
| from matplotlib import pyplot as plt | |
| from scipy.stats import gaussian_kde | |
| dirname = os.path.dirname(__file__) | |
| # Load the csv file into a pandas DataFrame | |
| papers_df = pd.read_csv( | |
| os.path.join(dirname, "data/nlp_papers_field_diversity.csv") | |
| ) | |
| # Compute the mean CFDI | |
| mean_cfdi = papers_df["incoming_diversity"].mean() | |
| # Compute the mean CADI | |
| mean_citation_ages = [] | |
| # Commenting out the old code | |
| #|# Open the file and read the content in a list | |
| #|with open( | |
| #| os.path.join(dirname, "data/nlp_papers_citation_age.txt"), | |
| #| "r", | |
| #| encoding="utf-8", | |
| #|) as filehandle: | |
| #| for line in filehandle: | |
| #| temp = float(line[:-1]) | |
| #| mean_citation_ages.append(temp) | |
| def generate_cfdi_plot(input_cfdi, compute_type="paper"): | |
| """ | |
| Function to generate a plot for CFDI | |
| """ | |
| # Using kdeplot to fill the distribution curve | |
| sns.set(font_scale=1.3, style="whitegrid") | |
| data = papers_df[papers_df["incoming_diversity"] > 0]["incoming_diversity"] | |
| kde = gaussian_kde(data) | |
| x_vals = np.linspace(data.min(), data.max(), 1000) | |
| y_vals = kde.evaluate(x_vals) | |
| fig, ax = plt.subplots() # create a new figure and axis | |
| ax.fill_between(x_vals, y_vals, color="skyblue", alpha=0.3) | |
| ax.plot(x_vals, y_vals, color="skyblue", linewidth=2, label="Distribution") | |
| interpolated_y_cfdi = np.interp(input_cfdi, x_vals, y_vals) | |
| ax.scatter( | |
| input_cfdi, | |
| interpolated_y_cfdi, | |
| c="r", | |
| marker="*", | |
| linewidths=2, | |
| zorder=2, | |
| s=32, | |
| ) | |
| ax.vlines( | |
| input_cfdi, | |
| 0, | |
| interpolated_y_cfdi, | |
| color="tomato", | |
| ls="--", | |
| lw=1.5, | |
| ) | |
| epsilon = 0.005 | |
| # Compute the average and plot it as a light grey vertical line | |
| mean_val = np.mean(data) | |
| # Interpolate the y value for the mean | |
| interpolated_y_mean = np.interp(mean_val, x_vals, y_vals) | |
| ax.vlines(mean_val, 0, interpolated_y_mean, color="grey", ls="--", lw=1.5) | |
| ax.text( | |
| mean_val + epsilon, | |
| interpolated_y_mean + epsilon, | |
| "Avg.", | |
| {"color": "grey", "fontsize": 13}, | |
| ha="left", # Horizontal alignment | |
| ) | |
| ax.text( | |
| input_cfdi + epsilon, | |
| interpolated_y_cfdi + epsilon, | |
| f"This {compute_type}", | |
| {"color": "#DC143C", "fontsize": 13}, | |
| ha="left", # Horizontal alignment | |
| ) | |
| ax.set_xlabel("Citation Field Diversity Index (CFDI)", fontsize=15) | |
| ax.set_ylabel("Density", fontsize=15) | |
| sns.despine(left=True, bottom=True, right=True, top=True) | |
| return fig | |
| def generate_maoc_plot(input_maoc, compute_type="paper"): | |
| """ | |
| Function to generate a plot for MAOC | |
| """ | |
| # Using kdeplot to fill the distribution curve | |
| sns.set(font_scale=1.3, style="whitegrid") | |
| data = pd.DataFrame(mean_citation_ages)[0] | |
| kde = gaussian_kde(data) | |
| x_vals = np.linspace(data.min(), data.max(), 1000) | |
| y_vals = kde.evaluate(x_vals) | |
| fig, ax = plt.subplots() # create a new figure and axis | |
| ax.fill_between(x_vals, y_vals, color="skyblue", alpha=0.3) | |
| ax.plot(x_vals, y_vals, color="skyblue", linewidth=2, label="Distribution") | |
| interpolated_y_cfdi = np.interp(input_maoc, x_vals, y_vals) | |
| ax.scatter( | |
| input_maoc, | |
| interpolated_y_cfdi, | |
| c="r", | |
| marker="*", | |
| linewidths=2, | |
| zorder=2, | |
| s=32, | |
| ) | |
| ax.vlines( | |
| input_maoc, | |
| 0, | |
| interpolated_y_cfdi, | |
| color="tomato", | |
| ls="--", | |
| lw=1.5, | |
| ) | |
| epsilon = 0.005 | |
| # Compute the average and plot it as a light grey vertical line | |
| mean_val = np.mean(data) | |
| # Interpolate the y value for the mean | |
| interpolated_y_mean = np.interp(mean_val, x_vals, y_vals) | |
| ax.vlines(mean_val, 0, interpolated_y_mean, color="grey", ls="--", lw=1.5) | |
| ax.text( | |
| mean_val + epsilon, | |
| interpolated_y_mean + epsilon, | |
| "Avg.", | |
| {"color": "grey", "fontsize": 13}, | |
| ha="left", # Horizontal alignment | |
| ) | |
| ax.text( | |
| input_maoc + epsilon, | |
| interpolated_y_cfdi + epsilon, | |
| f"This {compute_type}", | |
| {"color": "#DC143C", "fontsize": 13}, | |
| ha="left", # Horizontal alignment | |
| ) | |
| ax.set_xlabel("Mean Age of Citation (mAoC)", fontsize=15) | |
| ax.set_ylabel("Density", fontsize=15) | |
| sns.despine(left=True, bottom=True, right=True, top=True) | |
| return fig | |