update plot styles

netlogo analysisP2.py

@@ -6,6 +6,7 @@
 # This script is used to analyze the results of the NetLogo simulation. It reads the results from the csv file and plots the results as histograms. 
 # It also calculates the standard deviation of the results and saves the results to a csv file.
 
+# Import the necessary libraries
 import pandas as pd
 from scipy import stats
 import matplotlib.pyplot as plt
@@ -15,18 +16,21 @@
 # save path for the results
 save_path = '/Users/benmazin/Code Dev/RP 4/netlogo results/'
 
-def plothist(data, title, xlabel, ylabel, save_path):
+def plothist(data, figureTitle, xlabel, ylabel, ax, label):
+
+    # Remove NaN values and create bins
     data = data[np.isfinite(data)]
     bins = np.arange(0.5, 101.5, 2)
 
+    # Plot the histogram
     hist_data, bin_edges = np.histogram(data, bins=bins, density=True)
-    plt.hist(bin_edges[:-1], bins=bins, weights=hist_data, alpha=0.5, color='g', edgecolor='black')
-
+    ax.hist(bin_edges[:-1], bins=bins, weights=hist_data, alpha=0.7, color='skyblue', edgecolor='black', linewidth=1.5)
     bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2
 
     # List of distributions to test
     distributions = [stats.norm, stats.lognorm, stats.expon, stats.beta, stats.gamma, stats.weibull_min]
 
+    # Fit the distributions to the data
     sse = {}
     for distribution in distributions:
         try:
@@ -40,28 +44,38 @@ def plothist(data, title, xlabel, ylabel, save_path):
         except Exception as e:
             print(f"Could not fit {distribution.name} due to {e}")
 
+    # Get the best fit
     best_fit = min(sse, key=sse.get)
     best_params = distributions[distributions.index(getattr(stats, best_fit))].fit(data)
 
+    # Plot the best fit
     x = np.linspace(min(data), max(data), 100)
     pdf = getattr(stats, best_fit).pdf(x, *best_params[:-2], loc=best_params[-2], scale=best_params[-1])
-    plt.plot(x, pdf, 'k', linewidth=2, label=f"{best_fit} fit")
+    ax.plot(x, pdf, 'k', linewidth=3, label=f"{best_fit} fit")
 
-    plt.title(f"{title}\nBest fit: {best_fit}")
-    plt.xlabel(xlabel)
-    plt.ylabel(ylabel)
-    plt.legend()
-    plt.savefig(save_path, dpi=300)
-    plt.close()
+    # Add the title and labels
+    ax.set_title(figureTitle, fontsize=20)
+    ax.set_xlabel(xlabel, fontsize=16)
+    ax.set_ylabel(ylabel, fontsize=16)
+    ax.legend(fontsize=14)
+    ax.tick_params(axis='x', labelsize=14)
+    ax.tick_params(axis='y', labelsize=10)
+    ax.text(0.05, 0.95, label, transform=ax.transAxes, fontsize=50, fontweight='bold', verticalalignment='top')
 
 #open the csv from the preanalysis
 results = pd.read_csv(save_path + 'results.csv')
 
 # Plot an individual distribution for each category and save to a png in the same directory
-plothist(results['ChemoTotalDead'], 'Total Dead with Doxorubicin Treatment', 'Total Dead', 'Frequency', save_path + 'ChemoTotalDeadHist.png')
-plothist(results['ChemoPercentInfect'], 'Infection with Doxorubicin Treatment', 'Most Infected at One Time', 'Frequency', save_path + 'ChemoMostInfectHist.png')
-plothist(results['NoChemoTotalDead'], 'Total Dead WITHOUT Doxorubicin Treatment', 'Total Dead', 'Frequency', save_path + 'NoChemoTotalDeadHist.png')
-plothist(results['NoChemoPercentInfect'], 'Infection WITHOUT Doxorubicin Treatment', 'Most Infected at One Time', 'Frequency', save_path + 'NoChemoMostInfectHist.png')
+fig, axs = plt.subplots(2, 2, figsize=(15, 10))
+plothist(results['ChemoTotalDead'], 'Total Dead with Doxorubicin Treatment', 'Total Dead', 'Frequency', axs[0, 0], 'A')
+plothist(results['NoChemoTotalDead'], 'Total Dead WITHOUT Doxorubicin Treatment', 'Total Dead', 'Frequency', axs[0, 1], 'B')
+plothist(results['ChemoPercentInfect'], 'Infection with Doxorubicin Treatment', 'Most Infected at One Time', 'Frequency', axs[1, 0], 'C')
+plothist(results['NoChemoPercentInfect'], 'Infection WITHOUT Doxorubicin Treatment', 'Most Infected at One Time', 'Frequency', axs[1, 1], 'D')
+
+# Save the figure
+plt.tight_layout(pad=1.0, h_pad=3.0, w_pad=2.0)
+plt.savefig(save_path + 'combined_histograms.png', dpi=300)
+plt.close()
 
 data = results