Add python script for json to csv conversion of TtoZ data

scripts_python/TtoZne.py

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+import json
+import numpy as np
+import matplotlib.pyplot as plt
+import csv
+import re
+import readBC
+import glob
+
+
+file_path = "TZ_data.json"
+
+with open(file_path, "r") as f:
+    data = json.load(f)
+
+T_values = {}
+
+for func in data["funcs"]:
+    for point in func["pts"]:
+        T = point["x"]
+        Z = point["y"]
+        if T not in T_values:
+            T_values[T] = []
+        T_values[T].append(Z)
+
+T_sorted = sorted(T_values.keys())
+Z_avg = [np.mean(T_values[T]) for x in T_sorted]
+# y_std = [np.std(x_values[x]) for x in x_sorted]
+
+
+# Save to CSV
+csv_file_path = "../average_TtoZ_curve.csv"
+with open(csv_file_path, "w", newline="") as csvfile:
+    writer = csv.writer(csvfile)
+    writer.writerow(["T", "Z_avg"])
+    writer.writerows(zip(T_sorted, Z_avg))
+
+# Plotting feature
+# plt.figure(figsize=(11, 8))
+
+# for func in data["funcs"]:
+#     fName = func["fName"]
+#     print(fName)
+#     match = re.match(r"Ne\s*=\s*([\d\.]+)E([+-]?\d+)", fName)
+#     if match:
+#         Ne, X = match.groups()
+#         formatted_label = rf"$n_e = {float(Ne):.1f} \times 10^{{{int(X)}}}$"
+#     else:
+#         formatted_label = fName  # Fallback in case of no match
+#     x = np.array([point["x"] for point in func["pts"]])
+#     y = np.array([point["y"] for point in func["pts"]])
+    # plt.plot(x, y, linestyle="--", alpha=0.6, label=formatted_label)
+
+# Plot the average curve
+# plt.errorbar(x_sorted, y_avg, y_std, capsize=3, color="black", linewidth=2, label="Average")
+# Basko = 22.5 * np.power(np.divide(x_sorted, 100.0), 0.6)
+# plt.plot(x_sorted, Basko, color="blue", linewidth=2, label="Basko's power law")
+
+# plt.xscale("log")  
+# plt.xlim([0.5, 100])
+# plt.ylim([0, 30])
+# plt.xlabel(f"Temperature ($\mathrm{{eV}}$)")
+# plt.ylabel(f"$\overline{{Z}}$")
+# plt.title(f"{data['title']}")
+# plt.legend()
+# plt.grid(True)
+# plt.savefig("TtoZ.pdf")
+# plt.show()
+
+# Collect all unique x values
+x_values = sorted(set(point["x"] for func in data["funcs"] for point in func["pts"]))
+
+# Initialize a dictionary to store y values for each Ne
+y_values_dict = {}
+
+# Process each function's data
+ne_list = []
+for func in data["funcs"]:
+    # Extract only the numerical part of Ne
+    ne_value = re.search(r"[\d\.E\+\-]+", func["fName"]).group(0)
+    ne_value = float(ne_value) * 1e6  # using conversion to m^-3
+    ne_list.append(float(ne_value))
+    y_values_dict[ne_value] = {x: None for x in x_values}
+    
+    for point in func["pts"]:
+        y_values_dict[ne_value][point["x"]] = point["y"]
+
+
+header = ["x"] + list(y_values_dict.keys())
+csv_data = []
+
+for x in x_values:
+    row = [x] + [y_values_dict[ne].get(x, "") for ne in y_values_dict]
+    csv_data.append(row)
+
+# Save to CSV
+csv_file_path = "../Zave_values_per_Ne.csv"
+with open(csv_file_path, "w", newline="") as csvfile:
+    writer = csv.writer(csvfile)
+    writer.writerow(header)  # Write heade
+    writer.writerows(csv_data)  # Write data rows
+
+
+
+
+
+# Plotting contour of T to Z per ne
+# import pandas as pd
+
+# # Define file path
+# file_path = "Zave_values_per_Ne.csv"
+
+# # Read the file and inspect its contents
+# df = pd.read_csv(file_path)
+
+# # Display the first few rows
+# df.head()
+# # Extract x values and Ne values (column headers)
+# x_values = df.iloc[:10, 0].values  # First column
+# Ne_values = np.array([float(col) for col in df.columns[1:]])  # Convert column names to float
+# # Ne_values = np.array([col for col in Ne_values])
+
+# # Extract y values as a 2D array
+# y_values = df.iloc[:10, 1:].values
+
+# # Create meshgrid for contour plot
+# X, Y = np.meshgrid(Ne_values, x_values)
+
+# # Plot contour
+# plt.figure(figsize=(8, 6))
+# cp = plt.contourf(X, Y, y_values, levels=20, cmap='viridis')
+# plt.colorbar(cp, label="Zave values")
+
+# # fileBC = glob.glob('../2025-03-10_11.23.25/bc.csv')
+# # time, n, u, T, TtoZ, Zinj = readBC.read(fileBC[0])
+# # plt.plot(n, T, color="black", linestyle="dashed", marker="o", markersize=4, label="Boundary Condition")
+# # plt.scatter(n[1], T[1], color="white", edgecolors="black", s=80, label="t = 0")  # White dot with black edge
+# # plt.scatter(n[-1], T[-1], color="cyan", edgecolors="black", s=80, label="t = end") 
+
+# # Labels and title
+# # plt.yscale("log")
+# plt.legend()
+# plt.xscale("log")  # Since Ne spans many orders of magnitude
+# plt.xlabel("Electron Density (ne)")
+# plt.ylabel("Temperature (eV)")
+# plt.title("Zave")
+# plt.savefig("Contour_Tin_Zave_BC.pdf")
+# plt.show()