finished figures

scripts/Paper/pr_figs.py

@@ -1,50 +1,88 @@
-
-import numpy as np
 import pandas as pd
-from matplotlib import pyplot as plt
+import matplotlib.pyplot as plt
+from scipy.optimize import curve_fit
+import numpy as np
+
+def quadratic_model(x, a, b, c):
+    return a * x**2 + b * x + c
+
+def add_model(this_df, this_axs, i, p, y="tp_precision", marker="r"):
+    x_values = this_df[p].values
+    y_values = this_df[y].values
+
+    # Ensure x_values are sorted along with their corresponding y_values
+    sorted_indices = np.argsort(x_values)
+    x_values_sorted = x_values[sorted_indices]
+    y_values_sorted = y_values[sorted_indices]
+
+    # Initial guess for the parameters
+    initial_guess = [1, 1, 1]
+
+    # Fit the model
+    params, _ = curve_fit(quadratic_model, x_values_sorted, y_values_sorted, p0=initial_guess)
+
+    # Generate a set of evenly spaced x values for a smoother curve
+    smooth_x_values = np.linspace(x_values_sorted.min(), x_values_sorted.max(), 500)
+    fitted_vals = quadratic_model(smooth_x_values, *params)
+
+    this_axs[i].plot(smooth_x_values, fitted_vals, marker, label='Fitted model', linewidth=2.5)
 
+def plot_pr(losses:pd.DataFrame, title="str"):
+    plot_params = ['volfrac', 'snr', 'cnr', 'particle_size', 'brightness', 'r']
+    titles		= ['Density $\phi$', 'SNR', 'CNR', 'Size ($\mu m$)', '$f_\mu$ (0-255)', 'Radius (pxls)']
 
+    fig,axs = plt.subplots(2,len(plot_params),  sharey=True)
+    plt.tight_layout(pad=0)
 
+    this_axs = axs[0,:].flatten()
+    for i, p in enumerate(plot_params):
+        this_df = losses[losses['type'].isin([p])]
 
+        add_model(this_df, this_axs, i, p, "tp_precision", 'k')
+        add_model(this_df, this_axs, i, p, "precision", 'r')
+
+        this_axs[i].scatter(x=p, y = 'precision', 	data=this_df, color='red', marker='>', alpha=0.5)
+        this_axs[i].scatter(x=p, y = 'tp_precision', data=this_df, color='black', marker='<', alpha=0.5)
+        this_axs[i].set_xticks([])
+
+        if i == 0: 
+            this_axs[i].set_ylabel("Precision", fontsize='large')
+            this_axs[i].set_yticks([0,0.25,0.5,0.75,1])
+            this_axs[i].set_ylim(-0.1,1.1)
+            this_axs[i].legend(["TP", "U-net"])
+
+    this_axs = axs[1,:].flatten()
+    for i, p in enumerate(plot_params):
+        this_df = losses[losses['type'].isin([p])]
+
+        add_model(this_df, this_axs, i, p, "tp_recall", 'k')
+        add_model(this_df, this_axs, i, p, "recall", 'r')
+
+
+        this_axs[i].scatter(x=p, 		y = 'tp_recall', data=this_df, color='black', marker='<', alpha=0.5)
+        this_axs[i].scatter(x=p, 		y = 'recall', 	data=this_df, color='red', marker='>', alpha=0.5)
+        this_axs[i].set_xlabel(titles[i], fontsize='large')
+        if i == 0: 
+            this_axs[i].set_ylabel("Recall", fontsize='large')
+
+    fig.set_figwidth(12)
+    fig.set_figheight(4)
+    fig.suptitle(title, fontsize='xx-large', y=1.05)
+    return fig
 
 def main():
-	name = "unet_tp_1160"
-	path = f"output/Paper/{name}.html"
-	losses = pd.read_html(path, index_col=0)[0]
-	print(losses)
-	# exit()
-
-	plot_params = ['volfrac', 'snr', 'cnr', 'particle_size', 'brightness', 'r']
-	titles		= ['Density $\phi$', 'SNR', 'CNR', 'Size ($\mu m$)', '$f_\mu$ (0-255)', 'Radius (pxls)']
-
-	fig,axs = plt.subplots(2,len(plot_params),  sharey=True)
-	plt.tight_layout(pad=0)
-
-	this_axs = axs[0,:].flatten()
-	for i, p in enumerate(plot_params):
-		this_df = losses[losses['type'].isin([p])]
-		this_axs[i].scatter(x=p, 		y = 'tp_precision', data=this_df, color='black', marker='<')
-		this_axs[i].scatter(x=p, 		y = 'precision', 	data=this_df, color='red', marker='>')
-		this_axs[i].set_xticks([])
-		if i == 0: 
-			this_axs[i].set_ylabel("Precision", fontsize='large')
-			this_axs[i].set_yticks([0,0.25,0.5,0.75,1])
-			this_axs[i].set_ylim(-0.1,1.1)
-			this_axs[i].legend(["TP", "U-net"])
-
-	this_axs = axs[1,:].flatten()
-	for i, p in enumerate(plot_params):
-		this_df = losses[losses['type'].isin([p])]
-		this_axs[i].scatter(x=p, 		y = 'tp_recall', data=this_df, color='black', marker='<')
-		this_axs[i].scatter(x=p, 		y = 'recall', 	data=this_df, color='red', marker='>')
-		this_axs[i].set_xlabel(titles[i], fontsize='large')
-		if i == 0: 
-			this_axs[i].set_ylabel("Recall", fontsize='large')
-
-	fig.set_figwidth(13)
-	fig.suptitle("Precisions and Recalls", fontsize='xx-large', y=1.05)
-	path = f"output/Paper/{name}.png"
-	plt.savefig(path)
+    names = ["unet_tp_1160", "unet_log_1161", "unet_att_log_1170"]
+    titles = ["Unet (TP)", "Unet (LOG)", "Attention Unet (LOG)"]
+    for name, title in zip(names, titles):
+        path = f"output/Paper/{name}.html"
+        losses = pd.read_html(path, index_col=0)[0]
+        print(losses)
+        # exit()
+
+        fig = plot_pr(losses, title)
+        path = f"output/Paper/{name}.png"
+        plt.savefig(path, bbox_inches="tight")
+        plt.clf()
 
 if __name__ == "__main__":
-	main()
+    main()

scripts/Paper/real_cnr.py

@@ -18,7 +18,7 @@
 
 # Create the colormap
 global GFP_CMAP
-GFP_CMAP = LinearSegmentedColormap
+GFP_CMAP = LinearSegmentedColormap.from_list("CustomCmap", colors, N=n_bins)
 
 def plot_with_side_view(scan, path):
 	projection = np.max(scan, axis=0)