JGonzalez
052a4dc05e
Small set of corrections to the tag v2.5. Includes changes to python scripts to plot data. Includes new 'fast' setup conditions that allow to output cases in an hour or so with still a good CFL condition and grid resolution.
147 lines
4.4 KiB
Python
147 lines
4.4 KiB
Python
import json
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import numpy as np
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import matplotlib.pyplot as plt
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import csv
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import re
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import readBC
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import glob
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file_path = "TZ_data.json"
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with open(file_path, "r") as f:
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data = json.load(f)
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T_values = {}
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for func in data["funcs"]:
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for point in func["pts"]:
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T = point["x"]
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Z = point["y"]
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if T not in T_values:
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T_values[T] = []
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T_values[T].append(Z)
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T_sorted = sorted(T_values.keys())
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Z_avg = [np.mean(T_values[T]) for T in T_sorted]
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# Save to CSV
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csv_file_path = "../average_TtoZ_curve.csv"
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with open(csv_file_path, "w", newline="") as csvfile:
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writer = csv.writer(csvfile)
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writer.writerow(["T", "Z_avg"])
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writer.writerows(zip(T_sorted, Z_avg))
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# Plotting feature
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# plt.figure(figsize=(11, 8))
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# for func in data["funcs"]:
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# fName = func["fName"]
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# print(fName)
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# match = re.match(r"Ne\s*=\s*([\d\.]+)E([+-]?\d+)", fName)
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# if match:
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# Ne, X = match.groups()
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# formatted_label = rf"$n_e = {float(Ne):.1f} \times 10^{{{int(X)}}}$"
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# else:
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# formatted_label = fName # Fallback in case of no match
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# x = np.array([point["x"] for point in func["pts"]])
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# y = np.array([point["y"] for point in func["pts"]])
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# plt.plot(x, y, linestyle="--", alpha=0.6, label=formatted_label)
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# Plot the average curve
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# plt.errorbar(x_sorted, y_avg, y_std, capsize=3, color="black", linewidth=2, label="Average")
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# Basko = 22.5 * np.power(np.divide(x_sorted, 100.0), 0.6)
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# plt.plot(x_sorted, Basko, color="blue", linewidth=2, label="Basko's power law")
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# plt.xscale("log")
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# plt.xlim([0.5, 100])
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# plt.ylim([0, 30])
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# plt.xlabel(f"Temperature ($\mathrm{{eV}}$)")
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# plt.ylabel(f"$\overline{{Z}}$")
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# plt.title(f"{data['title']}")
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# plt.legend()
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# plt.grid(True)
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# plt.savefig("TtoZ.pdf")
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# plt.show()
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# Collect all unique x values
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x_values = sorted(set(point["x"] for func in data["funcs"] for point in func["pts"]))
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# Initialize a dictionary to store y values for each Ne
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y_values_dict = {}
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# Process each function's data
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ne_list = []
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for func in data["funcs"]:
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# Extract only the numerical part of Ne
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ne_value = re.search(r"[\d\.E\+\-]+", func["fName"]).group(0)
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ne_value = float(ne_value) * 1e6 # using conversion to m^-3
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ne_list.append(float(ne_value))
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y_values_dict[ne_value] = {x: None for x in x_values}
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for point in func["pts"]:
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y_values_dict[ne_value][point["x"]] = point["y"]
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header = ["x"] + list(y_values_dict.keys())
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csv_data = []
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for x in x_values:
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row = [x] + [y_values_dict[ne].get(x, "") for ne in y_values_dict]
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csv_data.append(row)
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# Save to CSV
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csv_file_path = "../Zave_values_per_Ne.csv"
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with open(csv_file_path, "w", newline="") as csvfile:
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writer = csv.writer(csvfile)
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writer.writerow(header) # Write heade
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writer.writerows(csv_data) # Write data rows
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# Plotting contour of T to Z per ne
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# import pandas as pd
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# # Define file path
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# file_path = "Zave_values_per_Ne.csv"
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# # Read the file and inspect its contents
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# df = pd.read_csv(file_path)
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# # Display the first few rows
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# df.head()
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# # Extract x values and Ne values (column headers)
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# x_values = df.iloc[:10, 0].values # First column
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# Ne_values = np.array([float(col) for col in df.columns[1:]]) # Convert column names to float
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# # Ne_values = np.array([col for col in Ne_values])
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# # Extract y values as a 2D array
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# y_values = df.iloc[:10, 1:].values
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# # Create meshgrid for contour plot
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# X, Y = np.meshgrid(Ne_values, x_values)
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# # Plot contour
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# plt.figure(figsize=(8, 6))
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# cp = plt.contourf(X, Y, y_values, levels=20, cmap='viridis')
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# plt.colorbar(cp, label="Zave values")
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# # fileBC = glob.glob('../2025-03-10_11.23.25/bc.csv')
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# # time, n, u, T, TtoZ, Zinj = readBC.read(fileBC[0])
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# # plt.plot(n, T, color="black", linestyle="dashed", marker="o", markersize=4, label="Boundary Condition")
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# # plt.scatter(n[1], T[1], color="white", edgecolors="black", s=80, label="t = 0") # White dot with black edge
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# # plt.scatter(n[-1], T[-1], color="cyan", edgecolors="black", s=80, label="t = end")
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# # Labels and title
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# # plt.yscale("log")
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# plt.legend()
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# plt.xscale("log") # Since Ne spans many orders of magnitude
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# plt.xlabel("Electron Density (ne)")
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# plt.ylabel("Temperature (eV)")
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# plt.title("Zave")
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# plt.savefig("Contour_Tin_Zave_BC.pdf")
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# plt.show()
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