import densmap as dm import matplotlib.pyplot as plt import numpy as np # FP = dm.fitting_parameters( par_file='parameters_shear.txt' ) FP = dm.fitting_parameters(par_file='parameters_shear.txt') # NB: contour tracking should check whether there are actually kfin-kinit files!!! CD = dm.shear_tracking(FP.folder_name, FP.first_stamp, FP.last_stamp, FP, \ file_root = '/flow_', contact_line = True, mode='int', ens=0) # Testing xmgrace output CD.save_xvg('InterfaceTest', mode='interface') # Testing cl distribution binning """ signal = np.array(CD.foot['tr'])[:,0] N = len(signal) N_in = 300 sign_mean, sign_std, bin_vector, distribution = \ dm.position_distribution( signal[N_in:], int(np.sqrt(N-N_in)) ) CD.plot_contact_line_pdf(N_in=300) """ # plt.step(bin_vector, distribution) # plt.show() # Testing plot CD.plot_radius() CD.plot_angles() # Movie
import densmap as dm import matplotlib.pyplot as plt import numpy as np FP = dm.fitting_parameters(par_file='ShearChar/parameters_shear.txt') # NB: conutour tracking should check whether there are actually kfin-kinit files!!! CD = dm.shear_tracking(FP.folder_name, FP.first_stamp, FP.last_stamp, FP, \ file_root = '/flow_', contact_line = True) CD.plot_radius() CD.plot_angles() dz = FP.dz # CD.movie_contour(FP.lenght_x, FP.lenght_z, dz, contact_line = True) CD.save_to_file('ShearDropModes')
import densmap as dm import matplotlib.pyplot as plt import numpy as np FP = dm.fitting_parameters(par_file='parameters_droplet.txt') # Rough substrate parameters # R15 """ height = 0.4 waven = 1.675135382692315 phi_0 = 1.5525217216960845 h_0 = 0.9129142857142857 fs = lambda x : height * np.sin(waven*x+phi_0) + h_0 dfs = lambda x : height * waven * np.cos(waven*x+phi_0) CD = dm.droplet_tracking(FP.folder_name, FP.first_stamp, FP.last_stamp, FP, \ file_root='/flow_', contact_line=True, f_sub=fs, df_sub=dfs) """ # R05 """ height = 0.1333333333333333 waven = 5.025696662822574 phi_0 = 4.638399152217284 h_0 = 0.6416666666666667 fs = lambda x : height * np.sin(waven*x+phi_0) + h_0 dfs = lambda x : height * waven * np.cos(waven*x+phi_0) CD = dm.droplet_tracking(FP.folder_name, FP.first_stamp, FP.last_stamp, FP, \ file_root='/flow_', contact_line=True, f_sub=fs, df_sub=dfs) """ # Flat substrate
import densmap as dm import numpy as np import matplotlib.pyplot as plt import scipy.optimize as opt file_root = 'flow_' FP = dm.fitting_parameters(par_file='parameters_test.txt') # folder_poiseuille = FP.folder_name+'LJPoiseuille/Flow_epsilon03_f6' # folder_couette = FP.folder_name+'LJCouette/Flow_epsilon03' folder_poiseuille = FP.folder_name + 'ConfinedPoiseuille_Q1_match/Flow' folder_couette = FP.folder_name + 'ConfinedCouette_Q1/Flow' Lx = FP.lenght_x Lz = FP.lenght_z vel_x, vel_z = dm.read_velocity_file(folder_poiseuille + '/' + file_root + '00001.dat') Nx = vel_x.shape[0] Nz = vel_x.shape[1] hx = Lx / Nx hz = Lz / Nz x = hx * np.arange(0.0, Nx, 1.0, dtype=float) + 0.5 * hx z = (hz * np.arange(0.0, Nz, 1.0, dtype=float) + 0.5 * hz) z_s = 1.2 z_f = 1.8 n_exclude = np.argmin(np.abs(z - z_f)) n_data = len(z) - n_exclude print("# exclude = " + str(n_exclude)) z = (hz * np.arange(0.0, Nz, 1.0, dtype=float) + 0.5 * hz) - 0.5 * Lz
import densmap as dm import matplotlib.pyplot as plt import numpy as np FP_1 = dm.fitting_parameters(par_file='parameters1.txt') FP_2 = dm.fitting_parameters(par_file='parameters2.txt') # FP_3 = dm.fitting_parameters( par_file='parameters.txt' ) CD = dm.droplet_tracking(FP_1.folder_name, FP_1.first_stamp, \ FP_1.last_stamp, FP_1) CD_2 = dm.droplet_tracking(FP_2.folder_name, FP_2.first_stamp, \ FP_2.last_stamp, FP_2) # CD_3 = dm.droplet_tracking(FP_3.folder_name, FP_3.first_stamp, \ # FP_3.last_stamp, FP_3) CD.merge(CD_2) # CD.merge(CD_3) CD.plot_radius() CD.plot_angles() dz = FP_1.dz CD.movie_contour(FP_1.lenght_x, FP_1.lenght_z, dz) # SAVING WHAT NEEDED spreading_radius = np.array(CD.foot_right) - np.array(CD.foot_left) mean_contact_angle = 0.5 * (np.array(CD.angle_right) + np.array(CD.angle_left)) hysteresis = np.array(CD.angle_right) - np.array(CD.angle_left) t = np.array(CD.time) spreading_radius = np.array(CD.spreading_radius) mean_contact_angle = np.array(CD.mean_contact_angle)
import densmap as dm import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt from matplotlib import cm file_root = 'flow_' # Linear flow profile fit print("[densmap] Obtaning density profile") FP = dm.fitting_parameters(par_file='parameters_nano.txt') folder_name = FP.folder_name Lx = FP.lenght_x Lz = FP.lenght_z rho = dm.read_density_file(folder_name + '/' + file_root + '00001.dat', bin='y') Nx = rho.shape[0] Nz = rho.shape[1] hx = Lx / Nx hz = Lz / Nz x = hx * np.arange(0.0, Nx, 1.0, dtype=float) z = hz * np.arange(0.0, Nz, 1.0, dtype=float) X, Z = np.meshgrid(x, z, sparse=False, indexing='ij') profile_density = np.zeros(len(z), dtype=float)