combustion_fluctuation/tools.py at master · AdamPI314/combustion_fluctuation · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
"""
tools, for example routine helping making figures
"""
import os

import matplotlib
matplotlib.use('Agg')
from matplotlib import pylab as plt
from matplotlib.lines import Line2D

import numpy as np
from naming import get_suffix
import interpolation


def get_colors_markers_linestyles():
    """
    return colors
    markers
    linestyles
    """
    markers_tmp = []
    for m_k in Line2D.markers:
        try:
            if len(m_k) == 1 and m_k != ' ':
                markers_tmp.append(m_k)
        except TypeError:
            pass

    markers_tmp = markers_tmp + [
        r'$\lambda$',
        r'$\bowtie$',
        r'$\circlearrowleft$',
        r'$\clubsuit$',
        r'$\checkmark$']

    markers = markers_tmp[2::]
    markers.append(markers_tmp[0])
    markers.append(markers_tmp[1])

    colors = ('b', 'g', 'k', 'c', 'm', 'y', 'r')
    linestyles = Line2D.lineStyles.keys()

    return colors, markers, linestyles


def make_figure_template(data_dir):
    """
    make_figure_template
    """
    colors, markers, _ = get_colors_markers_linestyles()
    # x axis file name
    f_n_x = "fname_x.csv"
    # y axis file name
    f_n_y = "fname_y.csv"
    # figure name
    fig_name = "test.jpg"

    data_x = np.loadtxt(os.path.join(data_dir, f_n_x),
                        dtype=float, delimiter=",")
    data_y = np.loadtxt(os.path.join(data_dir, f_n_y),
                        dtype=float, delimiter=",")
    # specify label for lines
    labels = ["line" + str(i + 1) for i in range(len(data_y))]

    delta_n = int(len(data_x) / 25)
    if delta_n is 0:
        delta_n = 1

    fig, a_x = plt.subplots(1, 1, sharex=True, sharey=False)
    for idx, _ in enumerate(data_y):
        a_x.plot(data_x[::delta_n], data_y[idx, ::delta_n],
                 color=colors[idx], marker=markers[idx], label=labels[idx])

    leg = a_x.legend(loc=0, fancybox=True, prop={'size': 10.0})
    leg.get_frame().set_alpha(0.7)

    a_x.set_xlim([data_x[0], data_x[-1]])
    a_x.grid()

    a_x.set_xlabel("1000/T(K$^{-1}$)")
    a_x.set_ylabel("k(cm$^{3}$ molecule$^{-1}$s$^{-1}$)")
    a_x.set_title("O$_2$ + npropyl")

    fig.tight_layout()
    fig.savefig(os.path.join(data_dir, fig_name), dpi=500)
    plt.close()


def pathway_time_2_array_index(data_dir, init_spe=None, atom_followed="C",
                               end_t=1.0, species_path=False, time=1.0):
    """
    pathway time converted to array index, pathway time read from pathway_time_canditate*
    """
    suffix = get_suffix(data_dir, init_spe=init_spe,
                        atom_followed=atom_followed, end_t=end_t)
    prefix = ""
    if species_path is True:
        prefix = "species_"

    f_n_path_time = os.path.join(
        data_dir, "output", prefix + "pathway_time_candidate" + suffix + ".csv")

    p_time = np.genfromtxt(f_n_path_time, dtype=float, delimiter=',')

    # in case of two dimensional pathway time
    if len(np.shape(p_time)) == 2:
        p_time = p_time[0, :]

    y_idx = [float(i) for i in range(len(p_time))]
    array_idx = interpolation.interp1d(p_time, y_idx, time)

    array_idx = int(array_idx)

    if array_idx >= len(p_time):
        array_idx = len(p_time) - 1
    if array_idx < 0:
        array_idx = 0

    return array_idx