I'm trying to plot interpolated velocity data from Fluent in Python. Interpolated velocity vector components are normalized by size and plotted with quiver function. I'm trying to represent the velocity magnitude with a colormap, but whatever I do, the arrows are black. Am I missing something? Sample code is below; the plot in question is at the end (axs[3]). import pandas as pd import numpy as np import scipy import matplotlib.pyplot as plt import matplotlib input_filename = 'period_rot_shadow_vel-mag-vel_comps.txt' df_results = pd.read_csv(input_filename, sep =',', skiprows = 1, names = ['nodenumber', 'X', 'Y', 'Z', 'vel_mag', 'v_x', 'v_y', 'v_z', 'v_ax', 'v_rad', 'v_tang']) x_data_min = df_results['X'].min() x_data_max = df_results['X'].max() y_data_min = df_results['Y'].min() y_data_max = df_results['Y'].max() x_size = np.abs(x_data_max) + np.abs(x_data_min) y_size = np.abs(y_data_max) + np.abs(y_data_min) rect_mesh_spacing = 0.001 N_x_steps = int(x_size/rect_mesh_spacing) + 1 N_y_steps = int(y_size/rect_mesh_spacing) + 1 rect_mesh_x_axis = np.array([]) rect_mesh_y_axis = np.array([]) for i in range(0, N_x_steps): x = x_data_min + i * rect_mesh_spacing rect_mesh_x_axis = np.append(rect_mesh_x_axis, x) for i in range(0, N_y_steps): y = y_data_min + i * rect_mesh_spacing rect_mesh_y_axis = np.append(rect_mesh_y_axis, y) #use meshgrid function to create a rectangular grid, meshgrid returns a tuple of ndarrays xx, yy = np.meshgrid(rect_mesh_x_axis, rect_mesh_y_axis) #create an array from meshgrid points xy_mesh = np.array((xx,yy)).T #define input coordinates (imporeted data point coordinates) as numpy arrays input_coords = np.array(list(zip(df_results['X'].to_numpy(), df_results['Y'].to_numpy()))) # scipy.spatial.KDTree function is used to lookup nearest-neighbour for defined points tree = scipy.spatial.KDTree(input_coords) dist, idx = tree.query(xy_mesh) #define the radius of lookup of vicinity of points radius = (rect_mesh_spacing + rect_mesh_spacing) / 2 distance_filter = dist <= radius #filter out the points in rectangular grid that are close to the exported data points xx_filtered, yy_filtered = xy_mesh[distance_filter].T v_x_interpol= scipy.interpolate.griddata((df_results['X'], df_results['Y']), df_results['v_x'], (xx_filtered, yy_filtered), method = 'nearest') v_y_interpol= scipy.interpolate.griddata((df_results['X'], df_results['Y']), df_results['v_y'], (xx_filtered, yy_filtered), method = 'nearest') v_mag_interpol = scipy.interpolate.griddata((df_results['X'], df_results['Y']), df_results['vel_mag'], (xx_filtered, yy_filtered), method = 'nearest') fig, axs = plt.subplots(1,4) fig.set_size_inches(13, 4) # Plot non-interpolated data axs[0].quiver(df_results['X'], df_results['Y'], df_results['v_x'], df_results['v_y']) # Plot interpolated data axs[1].quiver(xx_filtered, yy_filtered, v_x_interpol, v_y_interpol) #Plot interpolated data with normalized vector sizes (all arrows of the same size) #formula bellow is ordinary way to normalize vectors to the size of 1, it just divides every vector by it's length r = (v_x_interpol**2 + v_y_interpol**2)**0.5 axs[2].quiver(xx_filtered, yy_filtered, v_x_interpol/r, v_y_interpol/r) # Plot interpolated data with normalized vectors and normalized colormap #colormap is normalized on the vector length in 2D r = (v_x_interpol**2 + v_y_interpol**2)**0.5 axs[3].quiver(xx_filtered, yy_filtered, v_x_interpol/r, v_y_interpol/r,r, norm = matplotlib.colors.Normalize(vmin = np.min(r), vmax = np.max(r)), cmap = 'bwr') plt.show() Continue reading...
