In [1]:
# import numpy as np
# # !/usr/bin/env python3
# # -*- coding: utf-8 -*-
# """
# Created on 20181219
# @author: zhangji
# Trajection of a ellipse, Jeffery equation.
# """
# %pylab inline
# pylab.rcParams['figure.figsize'] = (25, 11)
# fontsize = 40
# import numpy as np
# import scipy as sp
# from scipy.optimize import leastsq, curve_fit
# from scipy import interpolate
# from scipy.interpolate import interp1d
# from scipy.io import loadmat, savemat
# # import scipy.misc
# import matplotlib
# from matplotlib import pyplot as plt
# from matplotlib import animation, rc
# import matplotlib.ticker as mtick
# from mpl_toolkits.axes_grid1.inset_locator import inset_axes, zoomed_inset_axes
# from mpl_toolkits.mplot3d import Axes3D, axes3d
# from sympy import symbols, simplify, series, exp
# from sympy.matrices import Matrix
# from sympy.solvers import solve
# from IPython.display import display, HTML
# from tqdm import tqdm_notebook as tqdm
# import pandas as pd
# import re
# from scanf import scanf
# import os
# import glob
# from codeStore import support_fun as spf
# from src.support_class import *
# from src import stokes_flow as sf
# rc('animation', html='html5')
# PWD = os.getcwd()
# font = {'size': 20}
# matplotlib.rc('font', **font)
# np.set_printoptions(linewidth=90, precision=5)
%load_ext autoreload
%autoreload 2
import os
import glob
import natsort
import numpy as np
import scipy as sp
from scipy.optimize import leastsq, curve_fit
from scipy import interpolate, integrate
from scipy import spatial, signal
# from scipy.interpolate import interp1d
from scipy.io import loadmat, savemat
# import scipy.misc
# import importlib
from IPython.display import display, HTML
import pandas as pd
import pickle
import re
from scanf import scanf
import matplotlib
# matplotlib.use('agg')
from matplotlib import pyplot as plt
import matplotlib.colors as colors
from matplotlib import animation, rc
import matplotlib.ticker as mtick
from mpl_toolkits.axes_grid1.inset_locator import inset_axes, zoomed_inset_axes
from mpl_toolkits.mplot3d import Axes3D, axes3d
from mpl_toolkits.axes_grid1.axes_divider import make_axes_locatable
from mpl_toolkits.mplot3d.art3d import Line3DCollection
from matplotlib import cm
from tqdm.notebook import tqdm as tqdm_notebook
from tqdm import tqdm
from time import time
from src.support_class import *
from src import jeffery_model as jm
from codeStore import support_fun as spf
from codeStore import support_fun_table as spf_tb
# %matplotlib notebook
%matplotlib inline
rc('animation', html='html5')
fontsize = 40
PWD = os.getcwd()
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fig = plt.figure(figsize=(2, 2))
fig.patch.set_facecolor('white')
ax0 = fig.add_subplot(1, 1, 1)
In [3]:
job_dir = 'ecoC01B05_wt0.1_psi-0a'
table_name = 'ecoC01B05_tao1_wm0.1'
t_headle = '(.*?).pickle'
In [4]:
t_dir = os.path.join(PWD, job_dir)
data = spf_tb.load_table_data_pickle_dir(t_dir, t_headle)
lst_eta = data.lst_eta
theta_max_fre = data.theta_max_fre
phi_max_fre = data.phi_max_fre
psi_max_fre = data.psi_max_fre
eta_max_fre = data.eta_max_fre
data_idx = data.data_idx.fillna(-1).astype(int)
In [13]:
# sort all frequrents
with np.printoptions(precision=10, suppress=True, threshold=1e10):
print(np.flipud(np.sort(data.theta_max_fre.values.flatten())))
In [45]:
# put images with same frequence into a subdirect
check_fre_list = [0.0160, 0.0170, 0.0190]
atol_fre_list = [0.0002, 0.0002, 0.0002]
Table_t_range = np.array((4500, np.inf))
def sub_seperate_0(i1, type_fre, iidx, Table_t_range):
t1 = np.zeros_like(iidx[0])
return t1
def sub_seperate_2(i1, type_fre, iidx, Table_t_range):
theta = type_fre.index.values[iidx[0]]
phi = type_fre.columns.values[iidx[1]]
theta_phi_list = np.vstack((theta, phi)).T
tuse = []
for theta, phi in tqdm_notebook(theta_phi_list, desc='No. %d ' % i1):
tpick, _ = spf_tb.load_table_date_pickle(job_dir, theta, phi)
Table_t = tpick['Table_t']
Table_theta = tpick['Table_theta']
Table_phi = tpick['Table_phi']
Table_psi = tpick['Table_psi']
idx = np.logical_and(Table_t >= Table_t_range[0], Table_t <= Table_t_range[1])
tuse.append(np.max(Table_theta[idx]))
tuse = np.hstack(tuse)
t1 = np.ones_like(tuse)
t1[tuse >1.9] = 0
t1[tuse < 1.844] = 2
return t1
# def sub_seperate_2(i1, type_fre, iidx, Table_t_range):
# theta = type_fre.index.values[iidx[0]]
# phi = type_fre.columns.values[iidx[1]]
# theta_phi_list = np.vstack((theta, phi)).T
# tuse = []
# for theta, phi in tqdm_notebook(theta_phi_list, desc='No. %d ' % i1):
# tpick, _ = spf_tb.load_table_date_pickle(job_dir, theta, phi)
# Table_t = tpick['Table_t']
# Table_theta = tpick['Table_theta']
# Table_phi = tpick['Table_phi']
# Table_psi = tpick['Table_psi']
# idx = np.logical_and(Table_t >= Table_t_range[0], Table_t <= Table_t_range[1])
# tuse.append(np.max(Table_phi[idx]))
# tuse = np.hstack(tuse)
# t1 = np.ones_like(tuse)
# t1[tuse < 4] = 0
# t1[tuse > 5.1] = 2
# return t1
sub_seperate = {0: sub_seperate_0,
1: sub_seperate_0,
2: sub_seperate_2,
3: sub_seperate_0, }
tfre = theta_max_fre.copy()
type_fre = tfre.copy()
type_fre.iloc[:, :] = -1
i0 = 0
process_total = 0
for i1, (check_fre, atol_fre) in enumerate(zip(check_fre_list[:], atol_fre_list)):
use_idx = np.isclose(tfre, check_fre, rtol=0, atol=atol_fre)
iidx = np.where(use_idx)
process_total = process_total + np.sum(use_idx)
t1 = sub_seperate[i1](i1, type_fre, iidx, Table_t_range)
type_fre.iloc[use_idx] = i0 + t1
i0 = i0 + 1 + np.max(t1)
print(i0, np.sum(use_idx))
# plot one of the remaind cases
if np.any(type_fre.values == -1):
for i3, i4 in np.vstack(np.where(type_fre.values == -1)).T:
type_fre.iloc[i3].iloc[i4] = i0
assert np.all(type_fre.values >= 0)
type_fre2 = type_fre.copy()
type_fre2.iloc[type_fre.values == 0] = 0
type_fre2.iloc[type_fre.values == 1] = 4
type_fre2.iloc[type_fre.values == 2] = 3
type_fre2.iloc[type_fre.values == 3] = 3
type_fre2.iloc[type_fre.values == 4] = 2
type_fre2.iloc[type_fre.values == 5] = 1
spf_tb.show_traj_phase_map_type(type_fre2)
# spf_tb.save_separate_angleList_fft(job_dir, tfre, check_fre_list, atol_fre_list)
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In [55]:
t1 = []
for i0 in np.arange(type_fre2.values.max() + 1):
t1.append(np.isclose(type_fre2.values, i0).sum())
print(np.sum(t1))
print(t1)
print(t1 / np.sum(t1))
In [56]:
fast_mode1 = 0
figsize = np.array((16, 9)) * 0.5
dpi = 200
tipical_th_ph_list = []
for i0 in np.arange(type_fre2.values.max() + 1)[:]:
iidx = np.where(np.isclose(type_fre2.values, i0))
spf_tb.phase_map_show_idx(type_fre2, tipical_th_ph_list, iidx, job_dir, table_name, fast_mode=fast_mode1, )
In [54]:
Table_t_range1 = np.array((9000, np.inf))
Table_t_range2 = np.array((9000, np.inf))
nshow = np.inf
# nshow = 2
fast_mode2 = True
figsize = np.array((16, 9)) * 0.5
dpi = 200
i0 = 3
iidx = np.where(np.isclose(type_fre2.values, i0))
spf_tb.phase_map_show_idx_list(type_fre2, iidx, job_dir, nshow=nshow, figsize=figsize, dpi=dpi,
Table_t_range1=Table_t_range1, Table_t_range2=Table_t_range2, fast_mode=fast_mode2, )
Out[54]:
In [48]:
Table_t_range1 = np.array((4500, 6000))
Table_t_range2 = np.array((4500, 6000))
nshow = np.inf
# nshow = 2
fast_mode2 = True
figsize = np.array((16, 9)) * 0.5
dpi = 100
for i0 in np.arange(type_fre2.values.max() + 1)[:]:
iidx = np.where(np.isclose(type_fre2.values, i0))
spf_tb.phase_map_show_idx_list(type_fre2, iidx, job_dir, nshow=nshow, figsize=figsize, dpi=dpi,
Table_t_range1=Table_t_range1, Table_t_range2=Table_t_range2, fast_mode=fast_mode2, )
In [33]:
# for i0 in np.arange(type_fre2.values.max() + 1)[:]:
i0 = 2
Table_t_range = (4500, np.inf)
iidx = np.where(np.isclose(type_fre2.values, i0))
theta = type_fre.index.values[iidx[0]]
phi = type_fre.columns.values[iidx[1]]
theta_phi_list = np.vstack((theta, phi)).T
t1 = []
for theta, phi in theta_phi_list:
tpick, _ = spf_tb.load_table_date_pickle(job_dir, theta, phi)
Table_t = tpick['Table_t']
idx = np.logical_and(Table_t >= Table_t_range[0], Table_t <= Table_t_range[1])
Table_theta = tpick['Table_theta'][idx]
Table_phi = tpick['Table_phi'][idx]
Table_psi = tpick['Table_psi'][idx]
t2 = (np.max(Table_theta), np.min(Table_theta), np.mean(Table_theta),
np.max(Table_phi), np.min(Table_phi), np.mean(Table_phi),
np.max(Table_psi), np.min(Table_psi), np.mean(Table_psi), )
t1.append(t2)
In [35]:
figsize = np.array((16, 9))
dpi = 50
t1 = np.vstack(t1)
t1[t1[:, 0] > 1.9, :] = np.nan
fig, axs = plt.subplots(3, 3, figsize=figsize, dpi=dpi)
for i0, axi in enumerate(axs.ravel()):
axi.plot(t1[:, i0], '.')
In [5]:
tipical_th_ph_list = [[0. , 1.714],
[0.000, 0. ],
[0.000, 0. ],
[0.143, 0.143],
[1.999, 6.140]]
tipical_th_ph_use_idx = [0, 2, 3, 4, ]
Table_t_range = np.array((4500, np.inf))
figsize = np.array((16, 9)) * 0.7
dpi = 200
tipical_th_ph_list_use = np.array(tipical_th_ph_list)[tipical_th_ph_use_idx]
label_list = np.arange(len(tipical_th_ph_list))[tipical_th_ph_use_idx]
spf_tb.show_table_theta_phi_psi_fft_list(tipical_th_ph_list_use, job_dir, label_list=label_list, figsize=figsize, dpi=dpi,
use_welch=False)
spf_tb.show_table_theta_phi_psi_fft_list(tipical_th_ph_list_use, job_dir, label_list=label_list, figsize=figsize, dpi=dpi,
use_welch=True)
spf_tb.show_table_result_list(tipical_th_ph_list_use, job_dir, label_list=label_list, Table_t_range=Table_t_range,
figsize=figsize, dpi=dpi)
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In [59]:
psi_lim_fct = 20
resampling_fct = 10
figsize = np.array((16, 9)) * 0.7
dpi = 200
# case_path_list = separate_fre_path_v2(type_fre2)
# color_list = [np.array(plt.get_cmap('tab20', len(check_fre_list))(i0)[:3]).reshape((1, 3))
# for i0 in np.arange(type_fre2.values.max() + 1)]
for idx, psi_lim1 in enumerate(np.linspace(0, 2 * np.pi, psi_lim_fct * 16, endpoint=False)[::psi_lim_fct]):
psi_lim = (psi_lim1, psi_lim1 + 2 * np.pi / (psi_lim_fct * 16))
# create fig
desc = '$\\psi \\in [%.3f \\pi, %.3f \\pi)$' % ((psi_lim[0] / np.pi), (psi_lim[1] / np.pi))
fig, ax1 = plt.subplots(1, 1, figsize=figsize, dpi=dpi, subplot_kw=dict(polar=True))
n_xticks = 32
xticks = np.arange(n_xticks)
ax1.set_xticks(xticks / n_xticks * 2 * np.pi)
ax1.set_xticklabels(['$\dfrac{%d}{%d}2\pi$' % (i0, n_xticks) for i0 in xticks])
ax1.set_yticklabels([])
ax1.set_ylim(0, np.pi)
fig.suptitle(desc)
fig.tight_layout(rect=[0, 0, 1, 0.95])
for i0 in np.arange(type_fre2.values.max() + 1):
# get case_path
iidx = np.where(np.isclose(type_fre2.values, i0))
theta = type_fre2.index.values[iidx[0]]
phi = type_fre2.columns.values[iidx[1]]
theta_phi_list = np.vstack((theta, phi)).T
case_path = []
for theta, phi in theta_phi_list[:]:
t_headle = 'th%5.3f_ph%5.3f_(.*?).pickle' % (theta, phi)
filename = [filename for filename in os.listdir(job_dir)
if re.match(t_headle, filename) is not None][0]
case_path.append(os.path.join(PWD, job_dir, filename))
thandle = ''
color = np.array(plt.get_cmap('tab20', int(type_fre2.values.max() + 1))(int(i0))[:3]).reshape((1, 3))
# print(int(type_fre2.values.max()), i0, color)
spf_tb.draw_phase_map_theta(case_path, color, psi_lim, axs=[ax1, ], thandle=thandle,
resampling=True, resampling_fct=resampling_fct)
tdir = os.path.join(PWD, job_dir, 'phase_mape_fre')
if not os.path.exists(tdir):
os.makedirs(tdir)
figname = os.path.join(tdir, '%04d.png' % (idx))
fig.savefig(os.path.join(tdir, figname))
print('save to %s' % figname)
plt.close(fig)
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