add missing files

Author: wilfonba

examples/1D_convergence/plot.py

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+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+N = [32, 64, 128, 256, 512, 1024]
+Ord = [1, 3, 5]
+
+errors = np.nan * np.zeros((len(N), len(Ord), 3))
+
+TEND = 200000
+
+for i in range(len(N)):
+    for j in range(len(Ord)):
+
+        sim_a1 = pd.read_csv(f"N{N[i]}_O{Ord[j]}/D/cons.5.00.{TEND}.dat", sep=r"\s+", header=None, names=["x", "y"])
+        sim_a2 = pd.read_csv(f"N{N[i]}_O{Ord[j]}/D/cons.6.00.{TEND}.dat", sep=r"\s+", header=None, names=["x", "y"])
+
+        exact_a1 = pd.read_csv(f"N{N[i]}_O{Ord[j]}/D/cons.5.00.000000.dat", sep=r"\s+", header=None, names=["x", "y"])
+        exact_a2 = pd.read_csv(f"N{N[i]}_O{Ord[j]}/D/cons.6.00.000000.dat", sep=r"\s+", header=None, names=["x", "y"])
+
+        ## 2 norm
+        errors[i, j, 0] = 1 / N[i] * np.sum(np.sqrt((sim_a1.y - exact_a1.y) ** 2))
+        errors[i, j, 0] += 1 / N[i] * np.sum(np.sqrt((sim_a2.y - exact_a2.y) ** 2))
+
+        ## 1 norm
+        errors[i, j, 1] = 1 / N[i] * np.sum(np.abs(sim_a1.y - exact_a1.y))
+        errors[i, j, 1] += 1 / N[i] * np.sum(np.abs(sim_a2.y - exact_a2.y))
+
+        ## Inf norm
+        errors[i, j, 2] = np.nanmax(np.abs(sim_a1.y - exact_a1.y))
+        errors[i, j, 2] += np.nanmax(np.abs(sim_a2.y - exact_a2.y))
+
+fig, ax = plt.subplots(1, 3, figsize=(12, 8), sharex=True)
+
+colors = ["blue", "green", "red", "purple"]
+
+ref = np.nan * np.zeros((len(N), len(Ord)))
+
+for i in range(3):
+    ax[i].plot(N, 30 / np.array(N) ** 1, label="Slope = -1", color=colors[0])
+    ax[i].plot(N, 3000 / np.array(N) ** 3, label="Slope = -3", color=colors[1])
+    ax[i].plot(N, 5000 / np.array(N) ** 5, label="Slope = -5", color=colors[2])
+
+for j in range(len(Ord)):
+    ax[0].plot(N, errors[:, j, 0], "o-", color=colors[j])
+    ax[0].set_xscale("log", base=2)
+    ax[0].set_yscale("log")
+    ax[0].set_title("||error||_2")
+    ax[0].legend()
+
+    ax[1].plot(N, errors[:, j, 1], "o-", color=colors[j])
+    ax[1].set_xscale("log", base=2)
+    ax[1].set_yscale("log")
+    ax[1].set_title("||error||_1")
+
+    ax[2].plot(N, errors[:, j, 2], "o-", color=colors[j])
+    ax[2].set_xscale("log", base=2)
+    ax[2].set_yscale("log")
+    ax[2].set_title("||error||_inf")
+
+plt.tight_layout()
+plt.show()
+
+errors = np.column_stack((N, errors[:, :, 0]))
+errors = np.column_stack((errors, 7 / np.array(N) ** 1))
+errors = np.column_stack((errors, 700 / np.array(N) ** 3))
+errors = np.column_stack((errors, 2000 / np.array(N) ** 5))
+
+df = pd.DataFrame(errors, columns=["N", "1", "3", "5", "R1", "R3", "R5"], index=N)
+df.to_csv("errors.csv", index=False)

examples/1D_convergence/submitJobs.sh

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+#!/usr/bin/env sh
+Nx=(32 64 128 256 512 1024)
+Order=(1 3 5)
+ME=2 # Model equations = 2 for five-equation model
+RS=2 # Riemann solver = 2 for HLLC
+
+#ROOT_DIR=<WORKING DIRECTORY>
+#MFC_DIR=<MFC ROOT DIR>
+ROOT_DIR="/Users/benwilfong/Documents/software/MFC-Wilfong/examples/1D_convergence"
+MFC_DIR="/Users/benwilfong/Documents/software/MFC-Wilfong"
+
+for i in "${Nx[@]}"; do
+    for j in "${Order[@]}"; do
+        rm -rf N${i}_O${j}
+        mkdir N${i}_O${j}
+        cp case.py N${i}_O${j}/
+    done
+done
+
+cd $MFC_DIR
+
+for i in "${Nx[@]}"; do
+    for j in "${Order[@]}"; do
+        ./mfc.sh run $ROOT_DIR/N${i}_O${j}/case.py --case-optimization --no-debug -- --order $j -N $i --meqns $ME --rs $RS
+    done
+done
+