Added plotting scripts for poster figures.

numerical/models/slot/slot_h_variation_only_plot.py

+import os
+
+import matplotlib.pyplot as plt
+import math
+import numpy as np
+
+import numerical.bem as bem
+import numerical.util.gen_utils as gen
+from common.util.plotting_utils import initialize_plt
+from common.util.file_utils import csv_to_lists
+import matplotlib.patches as patches
+
+initialize_plt()
+
+Hs = np.linspace(1, 5, 5)
+W = 2
+Xs = np.linspace(-3 * W, 3 * W, 300)
+Y = 2
+
+normalize = True
+
+m_0 = 1
+n = 20000
+
+fig_width = 3.5
+fig = plt.figure(figsize=(fig_width, 2 * fig_width / 3))
+
+fig.patch.set_facecolor('white')
+ax = fig.gca()
+xs = Xs / (0.5 * W)
+
+for i, H in enumerate(Hs):
+    print(f"Reading H = {H}")
+    f_dir = "../model_outputs/slot_h_collapse/"
+    f_name = f"h_collapse_n{n}_H{H}.csv"
+    xs, thetas = csv_to_lists(f_dir, f_name, True)
+
+    theta_star, x_star = sorted(zip(thetas, xs), key=lambda k: k[0])[-1]
+    norm_x_to_plot = np.divide(xs, x_star)
+    norm_theta_to_plot = np.divide(thetas, theta_star)
+
+    label_frac = "h"
+    ax.plot(xs, thetas, label=f"${label_frac} = {H / W:.2f}$")
+
+label_pad = 0
+ax.set_xlabel("$x$", labelpad=label_pad)
+ax.set_ylabel("$\\theta$ (rad)", labelpad=label_pad)
+ax.axvline(x=-1, linestyle='--', color='gray')
+ax.axvline(x=1, linestyle='--', color='gray')
+ax.legend(shadow=False, frameon=False, loc='lower right', labelspacing=0.1)
+plt.annotate(f"$y = 1$", xy=(0, 0), xytext=(0.025, 0.97),
+             textcoords='axes fraction',
+             horizontalalignment='left', verticalalignment='top')
+plt.tight_layout()
+plt.show()

numerical/models/slot/slot_jet_angle_contour_with_comparison.py

+import matplotlib.pyplot as plt
+from mpl_toolkits.axes_grid1 import make_axes_locatable
+import numpy as np
+
+from common.util.plotting_utils import initialize_plt
+from common.util.file_utils import csv_to_lists
+
+n = 20000
+W = 2.20
+H = 2.70
+density_ratio = 0.1
+w_thresh = 12
+length = 100
+
+N = 64
+
+Xs = []
+Ys = []
+us = []
+vs = []
+speeds = []
+
+file = open(f"../model_outputs/slot_vel_data/vel_sweep_n{n}_W{W:.2f}_H{H:.2f}"
+            f"_drat{density_ratio}_wthresh{w_thresh}_len{length}_N{N}.csv", 'r')
+
+for line in file.readlines():
+    X, Y, u, v = line.split(',')
+    Xs.append(float(X))
+    Ys.append(float(Y))
+    us.append(float(u))
+    vs.append(float(v))
+    speeds.append(np.linalg.norm([float(u), float(v)]))
+
+Xs, Ys, us, vs, speeds = zip(*sorted(zip(Xs, Ys, us, vs, speeds), key=lambda q: (q[1], q[0])))
+
+if len(Xs) == N ** 2:
+    X_mat = np.reshape(np.array(Xs), (N, N))
+    Y_mat = np.reshape(np.array(Ys), (N, N))
+    S = np.reshape(np.array(speeds), (N, N))
+    U = np.reshape(np.array(us), X_mat.shape)
+    V = np.reshape(np.array(vs), X_mat.shape)
+else:
+    X_mat = np.empty((N, N))
+    X_mat.fill(np.nan)
+    Y_mat = np.empty((N, N))
+    Y_mat.fill(np.nan)
+    U = np.empty((N, N))
+    U.fill(np.nan)
+    V = np.empty((N, N))
+    V.fill(np.nan)
+    S = np.empty((N, N))
+    S.fill(np.nan)
+    for k in range(len(Xs)):
+        i = int(np.floor(k / N))
+        j = int(k % N)
+        X_mat[i, j] = Xs[k]
+        Y_mat[i, j] = Ys[k]
+        U[i, j] = us[k]
+        V[i, j] = vs[k]
+        S[i, j] = speeds[k]
+
+c_xs = []
+c_ys = []
+c_zs = []
+centroids_file = open(f"../model_outputs/slot_vel_data/centroids_n{n}_W{W:.2f}_H{H:.2f}"
+                      f"_drat{density_ratio}_wthresh{w_thresh}_len{length}.csv", 'r')
+for line in centroids_file.readlines():
+    split = line.split(",")
+    c_xs.append(float(split[0]))
+    c_ys.append(float(split[1]))
+    c_zs.append(float(split[2]))
+
+n_xs = []
+n_ys = []
+n_zs = []
+normals_file = open(f"../model_outputs/slot_vel_data/normals_n{n}_W{W:.2f}_H{H:.2f}"
+                    f"_drat{density_ratio}_wthresh{w_thresh}_len{length}.csv", 'r')
+for line in normals_file.readlines():
+    split = line.split(",")
+    n_xs.append(float(split[0]))
+    n_ys.append(float(split[1]))
+    n_zs.append(float(split[2]))
+
+min_z = np.min([z for z, x in zip(c_zs, c_xs) if min(Xs) <= x <= max(Xs)])
+c_xs, c_ys, c_zs, n_xs, n_ys, n_zs = zip(*[o for o in zip(c_xs, c_ys, c_zs, n_xs, n_ys, n_zs)
+                                           if o[2] == min_z and min(Xs) <= o[0] <= max(Xs)])
+angles = np.arctan2(n_ys, n_xs) - np.pi / 2
+
+initialize_plt()
+
+###############
+# NORMAL PLOT #
+###############
+# NOTE: In the main plot (ax) all y values are doubled and then corrected in the tick labels to make the scaling work
+#       correctly. Other methods cause various axes to become the wrong sizes.
+fig, ax = plt.subplots(figsize=(5.31445, 4.3))
+ax.set_aspect(1, 'box')
+min_y_idx = 2
+max_y_idx = -10
+cnt = plt.contourf((2 * X_mat / W)[min_y_idx:max_y_idx, :], (2 * Y_mat / W)[min_y_idx:max_y_idx, :],
+                   (np.arctan2(V, U) + np.pi / 2)[min_y_idx:max_y_idx, :], levels=64,
+                   cmap=plt.cm.get_cmap('seismic'))
+for c in cnt.collections:
+    c.set_edgecolor("face")  # Reduce aliasing in output.
+# plt.scatter((2 * P / w)[min_q_idx:, :], (Q / w)[min_q_idx:, :], color='k', marker='.')
+plt.ylabel("$y$")
+plt.xlim((2 * min(Xs) / W, 2 * max(Xs) / W))
+plt.ylim(-2 * H / W - 0.1, 2 * np.max(Y_mat[min_y_idx:max_y_idx, :]) / W)
+plt.plot([min(Xs) * 2 / W, -1, -1, 1, 1, max(Xs) * 2 / W], [0, 0, -2 * H / W, -2 * H / W, 0, 0], 'k')
+Yticks = np.array(range(-2, int(np.max(Y_mat[min_y_idx:max_y_idx, :])) + 1, 2))
+ax.set_yticks(Yticks)
+ax.set_yticklabels(Yticks / 2)  # Hack to get scaling and sizing right.
+plt.scatter(np.divide(c_xs, 0.5 * W), np.divide(c_ys, 0.5 * W), marker='.', color='k')
+# for x, y, angle in zip(xs, ys, angles):
+#     plt.scatter(x, y, marker=(3, 0, np.degrees(angle)), color='k')
+plt.tick_params(axis='x', which='both', labelbottom=False)
+target_y = 1.28
+Y_idx = int(np.argmin(np.abs(np.subtract(np.divide(Ys, W), target_y))))
+line_y = Ys[Y_idx] / W
+
+plt.axhline(2 * line_y, color='k', linestyle='--')
+plt.annotate(f"($a$)", xy=(0, 0), xytext=(0.025, 0.965),
+             textcoords='axes fraction',
+             horizontalalignment='left', verticalalignment='top')
+
+divider = make_axes_locatable(ax)
+cax = divider.append_axes('right', size='3%', pad=0.1)
+# plt.colorbar(cnt, label="$\\theta$ (rad)", cax=cax, ticks=[-np.pi / 4, -np.pi / 8, 0, np.pi / 8, np.pi / 4])
+cbar = plt.colorbar(cnt, label="$\\theta$ (rad)", cax=cax, ticks=[-np.pi / 4, -np.pi / 8, 0, np.pi / 8, np.pi / 4])
+cax.set_yticklabels(["$-\\pi / 4$", "$-\\pi / 8$", "0", "$\\pi / 8$", "$\\pi / 4$"])
+# cbar = plt.colorbar(cnt, label="$\\theta$ (rad)", cax=cax, ticks=[-np.pi / 8, 0, np.pi / 8])
+# cax.set_yticklabels(["$-\\pi / 8$", "0", "$\\pi / 8$"])
+
+lax = divider.append_axes('bottom', 1.2, pad=0.1, sharex=ax)
+thetas = np.arctan2(vs, us) + np.pi / 2
+line_Xs, line_Ys, line_thetas = zip(*[(X, Y, theta) for X, Y, theta in zip(Xs, Ys, thetas) if Y / W == line_y])
+lax.plot(2 * np.divide(line_Xs, W), line_thetas, 'k--', label=f"Numerical")
+
+exp_file_dir = "../../../experimental/plotting/sweeps/mean_data/"
+exp_file_name = "mean_sweep_W2H3a_Y2.81.csv"
+mean_x, mean_theta, err = csv_to_lists(exp_file_dir, exp_file_name, has_headers=True)
+lax.errorbar(mean_x, mean_theta, yerr=err, fmt=".", c="k", label=f"Experimental")
+
+plt.xlabel("$x$")
+plt.ylabel("$\\theta$ (rad)")
+plt.legend(frameon=False, loc='lower right')
+plt.annotate(f"($b$) $y = {line_y:.2f}$", xy=(0, 0), xytext=(0.025, 0.93),
+             textcoords='axes fraction',
+             horizontalalignment='left', verticalalignment='top')
+plt.tight_layout()
+plt.show()

numerical/models/slot/slot_y_variation_only_plot.py

+import matplotlib.pyplot as plt
+import numpy as np
+
+from common.util.file_utils import csv_to_lists
+from common.util.plotting_utils import initialize_plt
+
+initialize_plt()
+
+H = 2
+W = 2
+Xs = np.linspace(-3 * W, 3 * W, 300)
+Ys = np.linspace(1, 5, 5)
+
+m_0 = 1
+n = 20000
+
+fig_width = 3.5
+fig = plt.figure(figsize=(fig_width, 2 * fig_width / 3))
+
+fig.patch.set_facecolor('white')
+ax = fig.gca()
+xs = Xs / (0.5 * W)
+
+for i, Y in enumerate(Ys):
+    print(f"Reading Y = {Y}")
+    f_dir = "../model_outputs/slot_y_collapse/"
+    f_name = f"y_collapse_n{n}_Y{Y}.csv"
+    xs, thetas = csv_to_lists(f_dir, f_name, True)
+
+    theta_star, x_star = sorted(zip(thetas, xs), key=lambda k: k[0])[-1]
+    norm_x_to_plot = np.divide(xs, x_star)
+    norm_theta_to_plot = np.divide(thetas, theta_star)
+
+    label_frac = "y"
+    ax.plot(xs, thetas, label=f"${label_frac} = {Y / W:.2f}$")
+
+label_pad = 0
+ax.set_xlabel("$x$", labelpad=label_pad)
+ax.set_ylabel("$\\theta$ (rad)", labelpad=label_pad)
+ax.axvline(x=-1, linestyle='--', color='gray')
+ax.axvline(x=1, linestyle='--', color='gray')
+ax.legend(shadow=False, frameon=False, loc='lower right', labelspacing=0.1)
+plt.annotate(f"$h = 1$", xy=(0, 0), xytext=(0.025, 0.97),
+             textcoords='axes fraction',
+             horizontalalignment='left', verticalalignment='top')
+plt.tight_layout()
+plt.show()