TopoAnalysis/plotting.py at master · stgl/TopoAnalysis · 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
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
"""Longitudinal- and chi-profile plotting helpers."""

import matplotlib.pylab as plt
import numpy as np

try:  # pragma: no cover - exercised by whichever import style is in use
    from . import dem as d
    from . import demRecursionTools
except ImportError:  # pragma: no cover
    import dem as d
    import demRecursionTools

def plot_downstream_profile(elevation, flow_direction, outlet, plot_code, downstream = True, start_at = 0.0, mean_pixel_dimension = None, figure = None):

    rc = flow_direction.search_down_flow_direction(outlet)

    rows = np.array(list(zip(*rc))[0], dtype=int)
    cols = np.array(list(zip(*rc))[1], dtype=int)

    if downstream:
        direction = 1.0
    else:
        direction = -1.0

    if mean_pixel_dimension is None:
        pixel_dimension = np.ones_like(rows) * flow_direction._georef_info.dx
    else:
        pixel_dimension = mean_pixel_dimension[rows, cols]

    for i in range(rows.shape[0]):
        if i == 0:
            length = np.array([start_at])
        else:
            if (rows[i] != rows[i-1]) & (cols[i] != cols[i-1]):
                length = np.append(length, np.array([length[i-1] + pixel_dimension[i-1] * 1.414 * direction]))
            else:
                length = np.append(length, np.array([length[i-1] + pixel_dimension[i-1] * direction]))

    elevation_profile = elevation._griddata[rows, cols]

    if figure is None:
        figure = plt.figure()

    plt.figure(figure.number)
    plt.plot(length, elevation_profile, plot_code)
    return rows, cols, length, elevation_profile

def plot_recursive_upstream_profiles(elevation, flow_direction, area, outlet, plot_code, downstream = False, start_at = 0.0, figure = None, minimum_area = 1.0E6):

    def plot_ld_link(current_length, ld_list, plot_code, downstream_sign, minimum_area):
        (current_row, current_column) = ld_list['index']


        if ld_list.get('next') is None:
            return
        for next_list in ld_list['next']:

            if next_list['area'] >= minimum_area:

                (next_row, next_column) = next_list['index']
                if (current_row != next_row) & (current_column != next_column):
                    next_length = current_length + (ld_list['de'] * 1.414 * downstream_sign)
                else:
                    next_length = current_length + (ld_list['de'] * downstream_sign)
                plt.plot([current_length, next_length], [ld_list['elevation'], next_list['elevation']], plot_code)
                plot_ld_link(next_length, next_list, plot_code, downstream_sign, minimum_area)

    mean_pixel_dimension = d.BaseSpatialGrid()
    mean_pixel_dimension._copy_info_from_grid(area, True)
    mean_pixel_dimension._griddata = area._mean_pixel_dimension()

    ld_list = flow_direction.map_values_to_recursive_list(outlet, elevation = elevation, area = area, de = mean_pixel_dimension)
    current_length = start_at
    if downstream:
        downstream_sign = 1.0
    else:
        downstream_sign = -1.0

    #if figure is None:
    #    figure = plt.figure()

    #plt.figure(figure.number)
    plot_ld_link(current_length, ld_list, plot_code, downstream_sign, minimum_area)


def plot_profiles(elevation, flow_direction, area, outlet, plot_code, minimum_area = 1.0E6, figure = None):

    if figure is None:
        figure = plt.figure()

    plot_downstream_profile(elevation, flow_direction, outlet, plot_code, mean_pixel_dimension= area._mean_pixel_dimension(), figure = figure)
    plot_recursive_upstream_profiles(elevation, flow_direction, area, outlet, plot_code, figure = figure, minimum_area = minimum_area)

def plot_profiles_with_outlet_code(prefix, code, plot_code, dem, fd, area, minimum_area = 1.0E7):

    import pickle
    outlets = pickle.load(open('outlets.p', 'rb'))
    outlet = outlets[prefix][code]
    plot_profiles(dem, fd, area, outlet, plot_code, minimum_area)

def plot_chi_profiles(elevation, flow_direction, area, outlet, plot_code, minimum_area = 1.0E6, figure = None, theta = 0.5, start_at = 0.0, downstream = True, Ao = 1.0E6):

    ((row, col),) = elevation._xy_to_rowscols((outlet,))
    base_elevation = elevation[row,col]

    def plot_ld_link(current_chi, ld_list, plot_code, downstream_sign, minimum_area):
        (current_row, current_column) = ld_list['index']

        if ld_list.get('next') is None:
            return
        for next_list in ld_list['next']:

            if next_list['area'] >= minimum_area:

                (next_row, next_column) = next_list['index']
                if (current_row != next_row) & (current_column != next_column):
                    next_chi = current_chi + (1 / np.array(next_list['area']))**theta * np.array(ld_list['de']*1.414*downstream_sign)

                else:
                    next_chi = current_chi + (1 / np.array(next_list['area']))**theta * np.array(ld_list['de']*downstream_sign)
                plt.plot([current_chi, next_chi], [(ld_list['elevation'] - base_elevation) * np.power(Ao,theta), (next_list['elevation'] - base_elevation)*np.power(Ao,theta)], plot_code)
                plot_ld_link(next_chi, next_list, plot_code, downstream_sign, minimum_area)

    mean_pixel_dimension = d.BaseSpatialGrid()
    mean_pixel_dimension._copy_info_from_grid(area, True)
    mean_pixel_dimension._griddata = area._mean_pixel_dimension()

    ld_list = flow_direction.map_values_to_recursive_list(outlet, elevation = elevation, area = area, de = mean_pixel_dimension)
    current_chi = start_at
    if downstream:
        downstream_sign = 1.0
    else:
        downstream_sign = -1.0

    if figure is None:
        figure = plt.figure()

    plt.figure(figure.number)
    plot_ld_link(current_chi, ld_list, plot_code, downstream_sign, minimum_area)

def plot_chi_profiles_with_outlet_code(prefix, code, plot_code, dem, fd, area, minimum_area = 1.0E7, theta = 0.5):

    import pickle
    outlets = pickle.load(open('outlets.p', 'rb'))
    outlet = outlets[prefix][code]
    plot_chi_profiles(dem, fd, area, outlet, plot_code, minimum_area = minimum_area, theta = theta)


def interactive_chi_profiles_and_map_view(prefix, code, plot_code, dem, fd, area, hillshade, minimum_area = 1.0E7, theta = 0.5, Ao = 1.0E6):


    import pickle
    outlets = pickle.load(open('outlets.p', 'rb'))
    outlet = outlets[prefix][code]

    chi_map = demRecursionTools.map_chi_profiles(dem, fd, area, outlet, minimum_area = minimum_area, theta = theta, Ao = Ao)
    indexes = chi_map.keys()
    import operator
    (chi, _) = list(zip(*operator.itemgetter(*indexes)(chi_map)))
    coordinates = list(zip(*hillshade._rowscols_to_xy(indexes)))

    fig1 = plt.figure(1)

    hillshade.plot(cmap = plt.cm.gray)
    plt.scatter(coordinates[0], coordinates[1], c=chi, s=0.5)
    ax = plt.gca()

    fig2 = plt.figure(2)
    plot_chi_profiles(dem, fd, area, outlet, plot_code, minimum_area = minimum_area, theta = theta, figure=fig2, Ao = Ao)

    current_marker = plt.plot(0,0,'bo');

    def hover(current_marker, Ao, theta):
        def hoverwrapper(event):
            if event.inaxes == ax:
                x = event.xdata
                y = event.ydata
                (i, ) = hillshade._xy_to_rowscols(((x, y), ))
                chi_elevation = chi_map.get(i)
                if chi_elevation is not None:
                    (chi, elevation) = chi_elevation
                    current_marker[0].set_data([chi], [elevation*np.power(Ao, theta)])

        return hoverwrapper

    fig1.canvas.mpl_connect('motion_notify_event', hover(current_marker, Ao, theta))