-
Notifications
You must be signed in to change notification settings - Fork 1
/
disturbance.py
182 lines (150 loc) · 7.75 KB
/
disturbance.py
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
import os
import logging
import arcpy
import numpy as np
import pandas as pd
import scipy.stats as ss
import settings as s
class Disturbance(object):
REFERENCE = s.ecocommunities
def __init__(self, year):
self.year = year
self._ecocommunities_filename = 'ecocommunities_%s.tif'
self.ecocommunities = None
self.ecocommunities_array = None
self.forest_age = None
self.canopy = None
self.dbh = None
self.community_table = pd.read_csv(s.COMMUNITY_TABLE, index_col=0)
self.dbh_lookup = pd.read_csv(s.DBH_LOOKUP, index_col=0)
self.upland_area = 0
refarray = arcpy.RasterToNumPyArray(self.REFERENCE)
self.shape = refarray.shape
self.set_ecocommunities()
self.set_canopy()
self.set_forest_age()
self.set_dbh()
def set_ecocommunities(self):
"""
set community raster for given year, if no raster exists use previous year,
else: use initial conditions community raster
"""
this_year_ecocomms = os.path.join(s.OUTPUT_DIR, self._ecocommunities_filename % self.year)
last_year_ecocomms = os.path.join(s.OUTPUT_DIR, self._ecocommunities_filename % (self.year - 1))
if os.path.isfile(this_year_ecocomms):
logging.info('disturbance set eco, using this year: {}'.format(this_year_ecocomms))
self.ecocommunities = arcpy.Raster(this_year_ecocomms)
elif os.path.isfile(last_year_ecocomms):
logging.info('disturbance set eco using last year: {}'.format(last_year_ecocomms))
self.ecocommunities = arcpy.Raster(last_year_ecocomms)
else:
logging.info('disturbance set eco initial run')
self.ecocommunities = arcpy.Raster(s.ecocommunities)
# self.ecocommunities.save(os.path.join(s.TEMP_DIR, 'temp.tif'))
def set_canopy(self):
"""
set canopy for given year if no canopy raster exists, use previous year,
else: initialize canopy raster
:return:
"""
if os.path.isfile(s.CANOPY):
logging.info('Setting canopy')
self.canopy = arcpy.RasterToNumPyArray(s.CANOPY)
else:
logging.info('Assigning initial values to canopy array')
if self.ecocommunities_array is None:
self.ecocommunities_array = arcpy.RasterToNumPyArray(self.ecocommunities)
self.canopy = np.empty(self.shape, dtype=np.int8)
# random canopy values for forests, shrublands and grasslands
# f = np.random.randint(low=51, high=100, size=(self.header['nrows'], self.header['ncols']))
# sh = np.random.randint(low=17, high=50, size=(self.header['nrows'], self.header['ncols']))
# g = np.random.randint(low=1, high=16, size=(self.header['nrows'], self.header['ncols']))
for index, row in self.community_table.iterrows():
self.canopy[self.ecocommunities_array == index] = row.max_canopy
# print row.max_canopy, type(row.max_canopy)
# if row.max_canopy > 50:
# self.canopy = np.where(self.ecocommunities_array == index, f, self.canopy)
# elif 20 < row.max_canopy <= 50:
# self.canopy = np.where(self.ecocommunities_array == index, sh, self.canopy)
# elif 0 < int(row.max_canopy) <= 20:
# self.canopy = np.where(self.ecocommunities_array == index, g, self.canopy)
# elif row.max_canopy == 0:
# self.canopy[self.ecocommunities_array == index] = row.max_canopy
canopy = arcpy.NumPyArrayToRaster(self.canopy,
arcpy.Point(arcpy.env.extent.XMin, arcpy.env.extent.YMin),
x_cell_size=s.CELL_SIZE,
y_cell_size=s.CELL_SIZE)
canopy.save(s.CANOPY)
def set_forest_age(self):
"""
set forest age for given year, if no forest age raster exists, use previous year,
else: initialize forest age raster
:return:
"""
if os.path.isfile(s.FOREST_AGE):
logging.info('Setting forest age')
self.forest_age = arcpy.RasterToNumPyArray(s.FOREST_AGE)
else:
logging.info('Assigning initial values to forest age array')
if self.ecocommunities_array is None:
self.ecocommunities_array = arcpy.RasterToNumPyArray(self.ecocommunities)
# create truncated normal distrbution for age
lower = s.MINIMUM_FOREST_AGE
upper = s.MAXIMUM_FOREST_AGE
mu = s.MEAN_INITIAL_FOREST_AGE
sigma = s.AGE_VAR
n = ss.truncnorm((lower - mu) / sigma, (upper - mu) / sigma, loc=mu, scale=sigma)
# populate an array with ages from distribution
tn = n.rvs(self.shape).astype(int)
self.forest_age = np.empty(shape=self.shape, dtype=np.int16)
# replace reset age for non-forest communities
for index, row in self.community_table.iterrows():
if row.forest == 1:
self.forest_age = np.where(self.ecocommunities_array == index, tn, self.forest_age)
forestage = arcpy.NumPyArrayToRaster(self.forest_age,
arcpy.Point(arcpy.env.extent.XMin, arcpy.env.extent.YMin),
x_cell_size=s.CELL_SIZE,
y_cell_size=s.CELL_SIZE)
forestage.save(s.FOREST_AGE)
def set_dbh(self):
"""
set DBH raster, if no raster exists initialize using age raster and dbh_lookup table
:return:
"""
if os.path.isfile(s.DBH):
logging.info('Setting dbh')
self.dbh = arcpy.RasterToNumPyArray(s.DBH)
else:
logging.info('Assigning initial values to dbh array')
self.dbh = np.empty(shape=self.shape, dtype=np.float32)
for index, row in self.community_table.iterrows():
# print("forest: %s" % row.forest)
# print("bool: ", row.forest == 1)
if row.forest == 1:
assert isinstance(self.ecocommunities_array, np.ndarray)
age = np.ma.masked_where(self.ecocommunities_array != index, self.forest_age)
# print(index)
# print(np.unique(age))
for a in np.ma.compressed(np.unique(age)):
d = self.dbh_lookup.ix[int(a)][str(index)]
self.dbh[(self.ecocommunities_array == index) & (self.forest_age == a)] = d
dbh = arcpy.NumPyArrayToRaster(self.dbh,
arcpy.Point(arcpy.env.extent.XMin, arcpy.env.extent.YMin),
x_cell_size=s.CELL_SIZE,
y_cell_size=s.CELL_SIZE)
dbh.save(s.DBH)
def set_upland_area(self):
if type(self.ecocommunities) is np.ndarray:
unique = np.unique(self.ecocommunities, return_counts=True)
else:
unique = np.unique(arcpy.RasterToNumPyArray(self.ecocommunities), return_counts=True)
area_hist = dict(zip(unique[0], (unique[1] * (s.CELL_SIZE ** 2) / 1000000.0)))
for index, row in self.community_table.iterrows():
if row.upland == 1 and index in area_hist:
self.upland_area += area_hist[index]
def hist(a):
if type(a) is np.ndarray:
values, counts = np.unique(a, return_counts=True)
else:
values, counts = np.unique(arcpy.RasterToNumPyArray(a, nodata_to_value=-9999), return_counts=True)
return dict(zip(values, counts))